Patent Application
20240361393
This application claims under 35 U.S.C. § 119(a) the benefit of Korean Patent Application No. 10-2023-0055862, filed on Apr. 28, 2023, the entire contents of which are incorporated herein by reference.
The present disclosure relates to manufacture of a battery for a vehicle, more particularly, to an evaluation system for manufacturing a dry electrode for the battery of the vehicle such as an electric vehicle.
Recently, a rechargeable secondary battery is expanding its application in various fields from a small electronic device to a large energy storage system. Particularly, with the rapid growth of the electric vehicle market, research and development on the secondary battery is being actively conducted.
The electrode of the secondary battery has been generally manufactured through a wet process. In the wet process, an electrode material, a binder, and a conductive additive contained in the electrode are dissolved in a solvent to prepare a slurry. Alternatively, a dry process is performed without using a solvent, which is needed in the wet process, and the dry process is capable of increasing an energy density of a battery compared to the wet process.
In the dry process of manufacturing an electrode, an electrode active material, a conductive additive, and a binder are mixed without a solvent to form a mixture, and then the mixture is formed into a dry electrode film using a press or calendaring method. Then the dry electrode film is attached to a current collector, thereby completing manufacturing of the electrode.
Because the manufacturing of the electrode using the dry process is in early stages of technological development, there is no standard technique to evaluate the quality of the electrode. For this reason, if a defect is found in a final stage of the manufacturing process, it is typically necessary to return to the first stage to solve the defect, thus increasing material costs and time in the manufacturing process.
The above information disclosed in this Background section is only for enhancement of understanding of the background of the disclosure, and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
The present disclosure provides an evaluation system for analyzing a dry electrode mixture, which is capable of measuring and/or analyzing characteristics of the dry electrode mixture during manufacture of the dry electrode.
In one aspect, a system for evaluating a dry electrode mixture according to the present disclosure includes an electrical conductivity measurement device configured to measure an electrical conductivity of the dry electrode mixture and an analysis device configured to evaluate the dry electrode mixture based on the measured electrical conductivity measurement.
A battery for a vehicle (e.g., an electric vehicle) may be produced from the above-described system. The battery may be a secondary battery for the electric vehicle.
A vehicle may include the battery as described above.
Other aspects and preferred embodiments of the disclosure are discussed infra.
The above and other features of the present disclosure will now be described in detail with reference to certain embodiments thereof illustrated in the accompanying drawings which are given hereinbelow by way of illustration only, and thus are not limitative of the present disclosure, and wherein:
It should be understood that the appended drawings are not necessarily to scale, presenting a somewhat simplified representation of various preferred features illustrative of the basic principles of the disclosure. The specific design features of the present disclosure as disclosed herein, including, for example, specific dimensions, orientations, locations, and shapes, will be determined in part by the particular intended application and usage environment.
In the figures, the reference numbers refer to the same or equivalent parts of the present disclosure throughout the several figures of the drawing.
It is understood that the term “vehicle” or “vehicular” or other similar term as used herein is inclusive of motor vehicles in general such as passenger automobiles including sports utility vehicles (SUV), buses, trucks, various commercial vehicles, watercraft including a variety of boats and ships, aircraft, and the like, and includes hybrid vehicles, electric vehicles, plug-in hybrid electric vehicles, hydrogen-powered vehicles and other alternative fuel vehicles (e.g. fuels derived from resources other than petroleum). As referred to herein, a hybrid vehicle is a vehicle that has two or more sources of power, for example both gasoline-powered and electric-powered vehicles.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Throughout the specification, unless explicitly described to the contrary, the word “comprise” and variations such as “comprises” or “comprising” will be understood to imply the inclusion of stated elements but not the exclusion of any other elements. In addition, the terms “unit”, “-er”, “-or”, and “module” described in the specification mean units for processing at least one function and operation, and can be implemented by hardware components or software components and combinations thereof.
Further, the control logic of the present disclosure may be embodied as non-transitory computer readable media on a computer readable medium containing executable program instructions executed by a processor, controller or the like. Examples of computer readable media include, but are not limited to, ROM, RAM, compact disc (CD)-ROMs, magnetic tapes, floppy disks, flash drives, smart cards and optical data storage devices. The computer readable medium can also be distributed in network coupled computer systems so that the computer readable media is stored and executed in a distributed fashion, e.g., by a telematics server or a Controller Area Network (CAN).
Descriptions of specific structures or functions presented in the embodiments of the present disclosure are merely exemplary for the purpose of explaining the embodiments according to the concept of the present disclosure, and the embodiments according to the concept of the present disclosure may be implemented in various forms. In addition, the descriptions should not be construed as being limited to the embodiments described herein, and should be understood to include all modifications, equivalents and substitutes falling within the idea and scope of the present disclosure.
Meanwhile, in the present disclosure, terms such as “first” and/or “second” may be used to describe various components, but the components are not limited by the terms. These terms are only used to distinguish one component from another. For example, a first component could be termed a second component, and similarly, a second component could be termed a first component, without departing from the scope of exemplary embodiments of the present disclosure.
It will be understood that, when a component is referred to as being “connected to” another component, the component may be directly connected to the other component, or intervening components may also be present. In contrast, when a component is referred to as being “directly connected to” another component, there is no intervening component present. Other terms used to describe relationships between components should be interpreted in a like fashion (e.g., “between” versus “directly between,” “adjacent” versus “directly adjacent,” etc.).
Throughout the specification, like reference numerals indicate like components. The terminology used herein is for the purpose of illustrating embodiments and is not intended to limit the present disclosure. In this specification, the singular form includes the plural sense, unless specified otherwise. The terms “comprises” and/or “comprising” used in this specification mean that the cited component, step, operation, and/or element does not exclude the presence or addition of one or more of other components, steps, operations, and/or elements.
Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings.
A dry electrode for a battery (e.g., a secondary battery of an electric vehicle) may be made from a dry electrode mixture and a current collector. The dry electrode may be manufactured in such a manner that the dry electrode mixture is introduced into a manufacturing apparatus 10, which is a film-forming facility, to be formed into a dry electrode film through a film forming process, and then the dry electrode film is attached or laminated to the current collector.
The dry electrode mixture comprises an electrode active material, a conductive additive, and a binder. The dry electrode mixture is prepared by mixing the electrode active material, the conductive additive, and the binder in a mixer. As a non-limiting example, the dry electrode mixture may be prepared in a high shear mixer using rotation or a fluid mixer using air. A dispersion condition may vary depending on a dispersing speed (rotational speed) of the mixer and dispersing time (operating time) of the mixer.
According to the present disclosure, the dry electrode may be a cathode or an anode.
In some implementations, in the cathode, the electrode active material comprises a cathode active material. As a non-limiting example, the cathode active material may be nickel manganese cobalt (NMC) series, lithium ferrophosphate (LFP), lithium cobalt (LCO), or sulfur. In some implementations, in the anode, the electrode active material comprises an anode active material. As a non-limiting example, the anode active material is graphite series and may comprise silicon.
The conductive additive may comprise a carbon-based material. In addition, for a dry electrode of an all-solid-state battery, the dry electrode mixture may further comprise a polyethylene oxide (PEO)-based polymer and an oxide-based and sulfide-based solid electrolyte. The binder may comprise polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF), or styrene butadiene rubber (SBR).
In forming a film of the dry electrode, the dispersion degree of the dry electrode mixture is crucial. A free-standing dry electrode film may be obtained only when the electrode material is properly dispersed, and the binder is fiberized to a predetermined degree. Even in a free-standing dry electrode film, when the dispersion degree is locally different, the cell performance of the battery may be deteriorated. Therefore, in order to obtain a dry electrode film that satisfies the quality standards, the proportion and dispersion degree of each constituent of the dry electrode mixture, and the fibrous degree of the binder need to be evaluated.
For this reason, the present disclosure provides a system for evaluating a dry electrode mixture which includes an electrical conductivity measurement device configured to measure an electrical conductivity of the dry electrode mixture and an analysis device capable of analyzing the measured results.
As described above, the dry electrode mixture comprise an electrode active material, a conductive additive, and a binder. Each constituent has a different inherent characteristic of electrical conductivity. Further, because the size of agglomerated binder, the dispersion degree of binder, and the like vary depending on the fibrous degree of the binder, the electrical conductivity characteristic may vary depending on the shape or density of the binder. The present disclosure is configured to evaluate the dry electrode mixture using such characteristics of the dry electrode mixture.
Fiberization of binder may be described as a change of round binder particles into thread-like binder particles while energy is applied to the dry electrode mixture in the process of mixing the dry electrode mixture. Specifically, the binder includes primary particles and secondary particles. Small binder particles of about 500 nanometers or less may be regarded as primary particles, and particles in which these primary particles are agglomerated to a size of approximately several hundred micrometers may be regarded as secondary particles. As energy is applied during the mixing process of the dry electrode mixture, the agglomerated secondary particles are separated into primary particles, and the primary particles change from a round shape to a thin thread or skein shape by high shear, which is called fiberization of binder.
In the fiberization process of the binder, the length of the thin thread may be long or short, thick or thin. Such degrees may be regarded as the fibrous degree of the binder. As described above, the fiberization of binder should be made to an appropriate level.
The fibrous degree of the binder may depend on the dispersion condition, such as the dispersing speed or dispersion time of a particular dry electrode mixture. For example, as the dispersing speed or the dispersion time increases in the same dry electrode mixture, the fibrous degree of the binder generally increases. Further, as the fiberization of the binder progresses, the electrical conductivity increases, converges to a predetermined level, and then decreases again. As the fiberization of the binder progresses, the thick thread-like tissue spreads thinly and an electron movement path may be secured, reducing the interparticle resistance and increasing the conductivity of the dry electrode mixture. However, when the fiberization of the binder progresses excessively, the thread-like tissue is coated on the electrode particles, increasing the resistance and decreasing the conductivity. Moreover, in order to attach a fiberized dry electrode mixture to a substrate, the fiberized dry electrode needs to be formed into a thin film. It is advantageous in forming such a thin film when the dry electrode mixture has a thread structure having a predetermined thickness or greater. In other words, excessive fiberization is disadvantageous in forming a film. The dispersing speed and dispersing time for appropriate fiberization may be defined for each dry electrode mixture, considering such characteristics.
As in
The electrical conductivity measurement device 20 is configured to measure an electrical conductivity of the dry electrode mixture M while applying a preset or variable pressure to the dry electrode mixture M. As illustrated in
Specifically,
The base rod 210 is fixed. To the base rod 210, components of the electrical conductivity measurement device 20 are movably and/or rotatably mounted. In some implementations, as illustrated in
The measurement space 220 is mounted to the base rod 210. The measurement space 220 may move upward and downward along the base rod 210. Further, the measurement space 220 may rotate about the base rod 210.
The measurement space 220 includes the probe 200 configured to measure an electrical conductivity. In an implementation, the measurement space 220 may have a support 222 provided therein, and the probe 200 may be installed in the support 222. The support 222 may be provided at a predetermined height in the measurement space 220. The support 222 may be separated from the measurement space 220 when the measurement space 220 moves. As illustrated in
The probe 200 may measure the electrical conductivity at multiple spots on the dry electrode mixture M. For example, the probe 200 may include at least four probes, and each of the probes 200 may measure the electrical conductivity of the dry electrode mixture M at its installed position.
The feeder 230 is mounted to the base rod 210. The feeder 230 may move along the base rod 210. Further, the feeder 230 may rotate around the base rod 210.
The feeder 230 may be supplied with the dry electrode mixture M manufactured in the manufacturing apparatus 10 for the dry electrode mixture M. Specifically, the dry electrode mixture M of a measurement target may be supplied to a receiving portion 232 in the feeder 230 through a supply hose 12 extending from the manufacturing apparatus 10.
As illustrated in
Referring to
The pressure applicator 240 may be mounted to the base rod 210. Specifically, the pressure applicator 240 may move along the base rod 210 and may rotate around the base rod 210. The pressure applicator 240 may adjust the pressure applied to the dry electrode mixture M while being moved along the base rod 210.
According to an implementation of the present disclosure, the electrical conductivity measurement device 20 may further include the remover 260. The remover 260 is configured to remove the dry electrode mixture M placed on the support 222. In an implementation, the remover 260 may remove the dry electrode mixture M from the support 222 using a suctioning method. In some implementations, the remover 260 may spray a predetermined amount of ethanol and the like while moving.
The remover 260 is mounted to the base rod 210. The remover 260 may also be movable along the base rod 210 and rotatable around the base rod 210.
According to an implementation of the present disclosure, the electrical conductivity measurement device 20 may further include the cleaner 270. The cleaner 270 may remove the dry electrode mixture M remaining in the measurement space 220.
The cleaner 270 is mounted to the base rod 210. The cleaner 270 may also be movable along the base rod 210, like the other components. Further, the cleaner 270 may rotate around the base rod 210. The cleaner 270 may include a cleaning element 272 and the cleaning arm 274. The cleaning element 272 may rotate with respect to the cleaning arm 274. The cleaning element 272 includes a brush and the likes and may remove the dry electrode mixture M remaining in the measurement space 220 while rotating and/or moving linearly.
The controller 250 is configured to control each of the components of the electrical conductivity measurement device 20. The controller 250 may control the rotation and the likes of the arms 224, 234, 242, 262, 274. and the rotating body 214. The controller 250 may control the movement and operation of each component of the measuring device 20 at a preset time point. Further, the controller 250 is configured to communicate with the analysis device 30 to transmit measurement results measured by the probe 200 to the analysis device 30.
The mass or volume of the dry electrode mixture M to be received in the receiving portion 232 may be controlled. For example, referring to
Referring again to
As illustrated in
Then, as illustrated in
As illustrated in
The tables below show that the measurements of conductivity are useful for the identification or evaluation of the dry electrode mixture M.
In order to determine whether even minute differences in the proportion of each element constituting the dry electrode mixture M may be distinguished by the electrical conductivity, the electrical conductivity was measured. The electrode active material and the binder are in weight percent and as shown in Table 1.
In Experimental Example 2, in addition to the electrode active material and the binder in Experimental Example 1, a conductive additive was further included in the mixture to measure the conductivity of the same as shown in Table 2.
In samples A and B, the electrode active material and the binder were changed by 0.5% by weight without changing the conductive additive. In samples B and C, the electrode active material and the binder were changed by 0.5% by weight without changing the conductive additive. In samples A and C, the electrode active material and the binder were changed by 1% by weight without changing the conductive additive. Samples D and E were cases where the binder was slightly increased by weight.
As shown in
According to the present disclosure, the analysis device 30 is configured to receive and analyze the measurement results measured by the electrical conductivity measurement device 20. According to the present disclosure, the analysis device 30 may identify any dry electrode mixture M by the measurement of the conductivity thereof.
To this end, the analysis device 30 is configured to learn data on the dry electrode mixture. Particularly, the analysis device 30 is configured to learn previously obtained datasets (e.g., obtained through experimentation) for a plurality of dry electrode mixture samples. Each dataset comprises mixture information on the dry electrode mixture (i.e., the proportion of each constituent, the dispersing speed, and the dispersing time of the dry electrode mixture). Particularly, the dry electrode mixture for each dataset may be a mixture previously determined (e.g., determined through experimentation) to be properly fiberized.
Specifically, samples of properly fiberized various constituents of the dry electrode mixture are prepared in different proportions, dispersing speeds, and dispersing times. Then the measuring device 20 measures the conductivity of each sample depending on the pressure applied thereto. Each dataset includes the proportion of the constituents, the dispersing speed (e.g., the rotational speed of the mixer), the dispersing time (e.g., the mixing time or operating time of the mixer) of the dry electrode mixture, and the conductivity depending on the pressure.
The plurality of datasets prepared in this way is learned by the analysis device 30. For example, the analysis device 30 performs machine learning on the datasets through an artificial intelligence model, and the learned artificial intelligence model is stored in the analysis device 30. The analysis device 30 may evaluate and predict the characteristics of the dry electrode mixture, which is the evaluation target, through the learned artificial intelligence model.
Accordingly, the evaluation system of the present disclosure may identify any dry electrode mixture (hereinafter, “dry electrode mixture to be evaluated”). For example, the evaluation system of the present disclosure may measure the electrical conductivity of the dry electrode mixture to be evaluated depending on the pressure applied thereto through the device 20, and may derive estimated mixture information on the dry electrode mixture through the analysis device 30 or the artificial intelligence model of the analysis device 30 based on the conductivity measurement. As described above, the estimated mixture information may include at least one of: the proportion of the constituents, the dispersing speed, or the dispersing time of the dry electrode mixture to be evaluated.
According to the evaluation system of the present disclosure, a dry electrode mixture, which is the evaluation target, may be evaluated based on estimated mixture information. In other words, an estimated proportion of each constituent, an estimated dispersing speed, and/or an estimated dispersing time of the dry electrode mixture to be evaluated may be obtained through the estimated mixture information.
Further, it may be determined whether the dry electrode mixture having the estimated mixture information satisfies the evaluation criterion. For example, whether the proportion of a constituent of the dry electrode mixture to be evaluated is appropriate may be determined by comparing the estimated proportion of the constituent of the dry electrode mixture to be evaluated with a preset proportion reference. Alternatively, whether the dispersion degree is appropriate may be determined by comparing the obtained estimated mixture information with a preset criterion.
Additionally, the fibrous degree of the binder of the dry electrode mixture to be evaluated may be evaluated based on the estimated mixture information. As described above, an appropriate fibrous degree may indicate that the dispersing speed and the dispersing time are sufficient, the electrical conductivity is greater than a predetermined value, and the dry electrode mixture may be made into a film. The analysis device 30 includes a processor 32 and a memory 34. Instructions executable by the processor 32 are stored in the memory 34. In an implementation of the present disclosure, the instructions may include instructions for executing the operation of the processor 32 and/or the operation of each component of the processor 32.
The memory 34 may be a volatile memory or a non-volatile memory. As a non-limiting example, the volatile memory may be dynamic random access memory (DRAM), static random access memory (SRAM), and the like. As another non-limiting example, the non-volatile memory may be an electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic RAM (MRAM), CD-ROM, DVD-ROM, and the like.
The processor 32 may execute instructions stored in the memory 34. The processor 32 may execute computer readable codes and instructions stored in the memory 34. As a non-limiting example, the processor 32 may include a central processing unit, a graphics processing unit, a neural processing unit, a multi-core processor, a multiprocessor, an application-specific integrated circuit (ASIC), and a field programmable gate array (FPGA).
According to some implementations of the present disclosure, the analysis device 30 may also be implemented in the form of a recording medium containing instructions executable by a computer, such as program modules executed by a computer. Computer readable media may be any available media accessible by a computer and includes both volatile and nonvolatile media, and removable and non-removable media. In addition, computer readable media may include all computer storage media. Computer storage media includes both volatile and nonvolatile media, and removable and non-removable media implemented through computer readable instructions, data structures, program modules, or any method or technology for storage of information such as data.
When mixture information on the measured dry electrode mixture is known, the number of measurements of the electrical conductivity may be reduced. According to some embodiments of the present disclosure, when information on the measured dry electrode mixture, such as the mixture proportion and dispersion condition of the measured dry electrode mixture M is known, the analysis device 30 may predict a conductivity in the entire pressure value range of the measured dry electrode mixture M using a logistic regression formula.
For example, when the conductivities of dry electrode mixture samples S1, S2, and S3 are clearly distinguished as shown in
According to some embodiments of the present disclosure, when mixture information such as the mixing proportion and dispersion condition of the measured dry electrode mixture M is unknown, the analysis device 30 may predict the electrical conductivity in the entire pressure value range of the measured dry electrode mixture M using a neural network model. As shown in
When mixture information on the dry electrode mixture M is not known, the analysis device 30 collects the electrical conductivity measured at at least three pressure values. Here, the three pressure values may be randomly designated, but it is preferable to select a pressure with the lowest value of mean absolute error (MAE) in the learning step. Further, the analysis device 30 may predict the electrical conductivity in the entire pressure range using a neural network model based on the conductivity measured at at least three pressure values. The predicted conductivity may be fed back in the mixing process of the dry electrode.
The analysis device 30 machine-learned in the above manner may predict electrical conductivity data in the entire pressure range of the dry electrode mixture M irrespective of whether the information on the dry electrode mixture M is known. This may greatly reduce the time spent on obtaining data.
The evaluation system according to the present disclosure may evaluate the proportion the constituents of and dispersion degree of the dry electrode mixture, and the fibrous degree of the binder by measuring the electrical conductivity. Because the binder acts as an insulator, a high binder proportion results in low conductivity. Further, in a case where the dry electrode mixture is not properly dispersed, the conductivity is measured to be low. When the proportion of the binder is low, the conductivity is measured to be high. In addition, even when the electrode active material, the conductive additive, and the binder are equal in proportion, the fibrous degree of the binder varies depending on the dispersion condition (dispersing speed, dispersing time, etc.), and in this case, the conductivities thereof are measured different.
As such, the evaluation system according to the present disclosure facilitates control of the quality of the dry electrode mixture by monitoring the dry electrode mixture in real time.
As is apparent from the above description, the present disclosure provides the following effect.
According to the present disclosure, there is provided an evaluation system for a dry electrode mixture capable of measuring and analyzing the characteristics of the dry electrode mixture during manufacture of the dry electrode.
Effects of the present disclosure are not limited to what has been described above, and other effects not mentioned herein will be clearly recognized by those skilled in the art based on the above description.
It will be apparent to those of ordinary skill in the art to which the present disclosure pertains that the present disclosure described above is not limited by the above-described embodiments and the accompanying drawings, and various substitutions, modifications and changes are possible within a range that does not depart from the technical idea of the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2023-0055862 | Apr 2023 | KR | national |
