Apparatus and method for predicting life of battery

Abstract

The invention relates to an apparatus and a method for predicting the lifetime of a battery. The method for predicting a life of a battery includes: calculating cumulative slip data based on life evaluation data of a target battery; calculating the correlation between the cumulative slip and the performance life of the target battery; and predicting the lifetime of the target battery based on the correlation.

Description

Technical Field

[0001] Various aspects of embodiments of this disclosure relate to an apparatus and method for predicting battery life. Background Technology

[0002] When developing batteries or rechargeable batteries, charging and discharging can be repeatedly performed under conditions and for periods similar to the actual use environment of the battery to ensure its lifespan. In this way, by measuring the battery's lifespan (or charge capacity retention rate), its long-term lifespan can be predicted, and its lifespan can be evaluated simultaneously. However, this method of evaluating battery lifespan is time-consuming and cumbersome. Therefore, it is desirable to provide a method for predicting battery lifespan that can shorten the time required for evaluating battery lifespan and detect battery failures at an early stage.

[0003] The information disclosed in this background section is intended to enhance the understanding of the background art of this disclosure, and therefore may contain information that does not constitute related (or prior art). Summary of the Invention

[0004] The problem to be solved by this disclosure is to provide an apparatus and method for predicting battery life in order to solve the above-mentioned problem.

[0005] These and other aspects and features of this disclosure will be described in, or will be apparent from, the following description of embodiments of this disclosure.

[0006] To address the above technical problems, according to some embodiments of this disclosure, a method for predicting battery life may include: calculating cumulative slip data based on life assessment data of a target battery; calculating the correlation between the cumulative slip and the performance life of the target battery; and predicting the life of the target battery based on the correlation.

[0007] According to some embodiments, lifetime assessment data may include voltage curve data regarding the capacity of the target battery over a range of 0 to 250 charge-discharge cycles.

[0008] According to some embodiments, calculating cumulative slip data may include: calculating cumulative capacity curve (CCP) data based on lifetime assessment data; and calculating cumulative slip based on cumulative capacity curve (CCP) data.

[0009] According to some embodiments, calculating cumulative capacity curve (CCP) data may include: calculating cumulative capacity curve (CCP) data based on multiple charge curves and multiple discharge curves extracted from lifetime assessment data.

[0010] According to some embodiments, each of the multiple charging curves may be a voltage curve corresponding to the capacity of the target battery in a single charging cycle, and each of the multiple discharging curves may be a voltage curve corresponding to the capacity of the target battery in a single discharging cycle.

[0011] According to some embodiments, calculating cumulative slip may include: calculating multiple charge slips and multiple discharge slips based on cumulative capacity curve (CCP) data, and calculating cumulative slip based on multiple charge slips and multiple discharge slips.

[0012] According to some embodiments, the cumulative slip can be the sum of the differences between each of the multiple charging slips and the corresponding discharge slip among the multiple discharging slips.

[0013] According to some embodiments, each of the plurality of charging slips can be the amount of change between the charging curve of the first charging cycle and the charging curve of a subsequent cycle of the first charging cycle, and each of the plurality of discharging slips can be the amount of change between the discharging curve of the first discharging cycle and the discharging curve of a subsequent cycle of the first discharging cycle.

[0014] According to some embodiments, calculating the correlation may include calculating the correlation between cumulative slip and the degradation trend of the target battery.

[0015] According to some embodiments, calculating the correlation may include using linear regression modeling techniques to calculate the correlation between cumulative slip and the performance lifetime of the target battery.

[0016] According to some embodiments, predicting the lifespan of a target battery may include: predicting the state of health (SOH) of the target battery based on charge-discharge cycles, based on the correlation between cumulative slip and the performance lifespan of the target battery.

[0017] To address the above technical problems, according to some embodiments of this disclosure, an apparatus for predicting battery life may include: a data generation module configured to generate cumulative slip data based on life assessment data of a target battery; a slip life analysis module configured to calculate the correlation between cumulative slip and the performance life of the target battery; and a life prediction module configured to predict the life of the target battery based on the correlation.

[0018] According to some embodiments, lifetime assessment data may include voltage curve data regarding the capacity of the target battery over a range of 0 to 250 charge-discharge cycles.

[0019] According to some embodiments, the data generation module may include: a CCP calculation module configured to calculate cumulative capacity curve (CCP) data based on lifetime assessment data; and a slip calculation module configured to calculate cumulative slip based on the cumulative capacity curve (CCP) data.

[0020] According to some embodiments, the CCP calculation module can be configured to calculate cumulative capacity curve (CCP) data based on multiple charge curves and multiple discharge curves extracted from lifetime assessment data.

[0021] According to some embodiments, the slip calculation module can be configured to calculate multiple charge slips and multiple discharge slips based on cumulative capacity curve (CCP) data.

[0022] According to some embodiments, the slip calculation module can be configured to calculate cumulative slip based on multiple charging slips and multiple discharging slips.

[0023] According to some embodiments, the slip lifetime analysis module can be configured to calculate the correlation between cumulative slip and the degradation trend of the target battery.

[0024] According to some embodiments, the slip lifetime analysis module can be configured to use linear regression modeling techniques to calculate the correlation between cumulative slip and the performance lifetime of the target battery.

[0025] According to some embodiments, the lifetime prediction module can be configured to predict the state of health (SOH) of the target battery based on charge-discharge cycles, based on the correlation between cumulative slip and the performance lifetime of the target battery.

[0026] According to some embodiments of this disclosure, the long-term lifespan of a battery can be easily predicted based on charging and discharging data from the battery's initial lifespan data.

[0027] According to some embodiments of this disclosure, the efficiency of battery manufacturing can be improved because the battery life can be predicted at an early stage.

[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 detailed description below that other aspects and features not mentioned will be apparent. Attached Figure Description

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

[0030] Figure 1 This is a block diagram illustrating a battery life assessment system including a device for predicting battery life according to some embodiments of the present disclosure.

[0031] Figure 2 This is a block diagram illustrating an information processing system used in a battery life assessment system according to some embodiments of the present disclosure.

[0032] Figure 3 This is a block diagram describing a device for predicting battery life according to some embodiments of the present disclosure.

[0033] Figure 4 This is a block diagram describing the configuration of a data generation module according to some embodiments of the present disclosure.

[0034] Figure 5 and Figure 6 These are examples describing the operation of a CCP calculation module according to some embodiments of the present disclosure.

[0035] Figure 7A and Figure 7B This is an example describing the operation of the sliding calculation module.

[0036] Figure 8 and Figure 9 This is an example describing the operation of a slip lifetime analysis module according to some embodiments of the present disclosure.

[0037] Figure 10 This is a flowchart describing a method for predicting battery life according to some embodiments.

[0038] Description of some figure labels

[0039] 1: Battery life assessment system

[0040] 12: Battery

[0041] 14: Life assessment data collection equipment

[0042] 16: Life Prediction Equipment

[0043] 120: Data Generation Module

[0044] 122: CCP Calculation Module

[0045] 124: Slip Calculation Module

[0046] 140: Slip Life Analysis Module

[0047] 160: Life Prediction Module

[0048] 200: Information Processing System

[0049] 210: Memory

[0050] 220: Processor

[0051] 230: Communication Module

[0052] 240: Input / Output Interface Detailed Implementation

[0053] 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 as meanings and concepts consistent with the technical spirit of the present disclosure, based on the principle that the inventor can be his / her own lexicographer to appropriately define the concepts of the terms in order to best interpret his / her invention.

[0054] The embodiments described in this specification and the configurations shown in the accompanying drawings are only some of the embodiments of this disclosure and do not represent all the technical ideas, aspects, and features of this disclosure. Therefore, it should be understood that at the time of filing this application, there may be various equivalent solutions and modifications that can replace or modify the embodiments described herein.

[0055] 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 one or more intermediary elements or layers may be present. When an element or layer is referred to as being "directly" on, directly connected to, or directly coupled to another element or layer, no intermediary element or layer is present. For example, when a first element is described as being "coupled to" or "connected to" a second element, the first element can be directly coupled to or directly 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.

[0056] In the figures, the dimensions of various 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 associated listed items. Furthermore, when describing embodiments of this disclosure, the use of “may” refers to “one or more of the embodiments of this disclosure.” When preceding / 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 of A, B, and C, 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 terms “use,” “being used,” and “being exploited” may be synonymous with the terms “utilizing,” “being exploited,” and “being exploited,” 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.

[0057] It will be understood that although the terms first, second, third, etc., may be used herein to describe various elements, components, regions, layers, and / or portions, these elements, components, regions, layers, and / or portions should not be limited by these terms. These terms are used to distinguish one element, component, region, layer, or portion from another element, component, region, layer, or portion. Therefore, without departing from the teachings of the exemplary embodiments, the first element, component, region, layer, or portion discussed below may be referred to as the second element, component, region, layer, or portion.

[0058] For ease of description, spatial relative terms such as “below,” “under,” “down,” “above,” and “above” may be used herein to describe the relationship between one element or feature as illustrated in the figures and another element(s). It will be understood that, in addition to the orientations depicted in the figures, the spatial relative terms are intended to cover different orientations of the device in use or operation. For example, if the device in the figures is flipped, the element described as “below” or “under” other elements or features will then be oriented “above” or “above” other elements or features. Thus, the term “below” can encompass both the above and below orientations. The device may adopt other orientations (rotated 90 degrees or in other orientations), and the spatial relative descriptors used herein should be interpreted accordingly.

[0059] 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” is intended to include the plural form as well, unless the context clearly indicates otherwise. It will be further understood that, when used in this specification, the terms “comprising” and / or “including” and 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.

[0060] Furthermore, any numerical range disclosed and / or described herein is intended to include all subranges with the same numerical precision contained 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, 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 contained therein, and any minimum numerical limit described in this specification is intended to include all higher numerical limits contained therein. Therefore, the applicant reserves the right to amend this specification (including the claims) to expressly describe any subranges included within the range expressly described herein.

[0061] Referring to two compared elements, features, etc., as “identical” can 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, this can mean that it is uniform in terms of its mean.

[0062] Throughout this specification, unless otherwise stated, each element may be singular or plural.

[0063] Placing any element "above (or below)" or "on (below)" another element can mean that the arbitrary element can be configured to contact the upper (or lower) surface of the element, and that another element can be inserted between the element and the arbitrary element positioned on (or below) the element.

[0064] Furthermore, it will be understood that when a component is referred to as “linked,” “coupled,” or “connected” to another component, these components can be directly “coupled,” “linked,” or “connected” to each other, or another component can be “inserted” between these components.

[0065] Throughout this specification, unless otherwise stated, when “A and / or B” is mentioned, it means A, B, or A and B. That is, “and / or” includes any or all combinations of the listed items. Unless otherwise stated, when “C to D” is mentioned, it means C or more and D or fewer.

[0066] Figure 1 This is a block diagram illustrating a battery life assessment system 1 including a device 16 for predicting battery life according to some embodiments of the present disclosure.

[0067] refer to Figure 1 The battery life assessment system 1 may include a battery 12, a life assessment data collection device 14, and a device 16 for predicting the battery life.

[0068] In some embodiments, the lifetime assessment data collection device 14 can collect lifetime assessment data of the target battery 12 from the manufacturing facility that manufactures the target battery 12 cells. For example, the lifetime assessment data collection device 14 can collect lifetime assessment data generated by the manufacturing facility when repeatedly performing charge and discharge operations on the target battery 12 cells. Here, the lifetime assessment data may include voltage curve data regarding the capacity of the target battery 12. As a specific example, the lifetime assessment data may be voltage curve data regarding the capacity of the target battery 12 over a range of 0 to 250 charge-discharge cycles. In other embodiments, the lifetime assessment data collection device 14 may include a charge-discharge module capable of performing charge and discharge operations on the target battery 12 manufactured by the manufacturing facility, and a data collection module collecting lifetime assessment data generated by the charge-discharge module when repeatedly performing charge and discharge operations on the target battery 12 cells.

[0069] The device 16 for predicting battery life can calculate slip-related data based on life assessment data. Here, slip can refer to the amount of change in capacity in the cumulative capacity-voltage curve data. For example, charge slip can refer to the amount of change in charging capacity in the cumulative capacity-voltage curve data, and discharge slip can refer to the amount of change in discharging capacity in the cumulative capacity-voltage curve data.

[0070] In some embodiments, the device 16 for predicting battery life can predict the lifespan of the target battery 12 based on slip correlation data. Here, the device 16 for predicting battery life can predict the lifespan of the target battery 12 based on the correlation between slip correlation data, including cumulative slip, and the degradation trend of the target battery 12. For example, the degradation trend may include, but is not limited to, points of sudden decline and EOL (end of life) points of the target battery 12. The degradation trend can include all characteristics indicating the degradation trend based on the lifespan of the target battery 12.

[0071] Linear regression modeling techniques can be used to calculate the correlation between cumulative slip and the performance lifespan of the target battery 12. Furthermore, the device 16 for predicting battery lifespan can predict the state of health (SOH) of the target battery 12 based on this correlation and according to charge-discharge cycles. The lifespan predicted by the device 16 for predicting battery lifespan can be used to assess the lifespan quality of the target battery 12.

[0072] As described above, the battery life assessment system 1 according to some embodiments of the present disclosure can easily predict the long-term life of the battery based on the initial life data of the battery.

[0073] According to some embodiments of this disclosure, since the battery life assessment system 1 can predict the battery life, the quality of the battery cell can be assessed at an early stage, and the efficiency of battery manufacturing can be improved.

[0074] Figure 2 This is a block diagram illustrating an information processing system 200 used in a battery life assessment system 1 according to some embodiments of the present disclosure.

[0075] refer to Figure 2 For example, information processing system 200 can be connected with... Figure 1 The battery life assessment system 1 shown corresponds to at least one or more of the devices 16 used to predict battery life. The information processing system 200 may include a memory 210, a processor 220, a communication module 230, and an input / output interface 240. (Reference) Figure 2 The information processing system 200 can be configured to transmit information and / or data over a network using the communication module 230. In some embodiments, the information processing system 200 may consist of at least one device including a memory 210, a processor 220, a communication module 230, and an input / output interface 240.

[0076] Memory 210 may include any non-transient computer-readable recording medium. In some embodiments, memory 210 may include a persistent mass storage device such as read-only memory (ROM), a disk drive, a solid-state drive (SSD), flash memory, etc. As another example, a persistent mass storage device such as ROM, SSD, flash memory, disk drive, etc., may be included in the information processing system 200 as a separate persistent storage device different from memory 210. Furthermore, memory 210 may store software components, including an operating system and at least one program code (e.g., code for implementing a slip lifetime analysis module and a lifetime prediction module installed and running in the information processing system 200).

[0077] These software components can be loaded from a computer-readable recording medium separate from memory 210. Such a separate computer-readable recording medium may include a recording medium that can be directly connected to information processing system 200, and may include, for example, computer-readable recording media such as floppy disk drives, magnetic disks, magnetic tapes, DVD / CD-ROM drives, memory cards, etc. As another example, software components may be loaded into memory 210 via communication module 230 instead of a computer-readable recording medium. For example, at least one program may be loaded into memory 210 based on a computer program (e.g., a program for implementing a slip-life analysis module and a life prediction module, etc.) installed by a file distribution system provided by the developer via communication module 230 or by distributing application installation files.

[0078] Processor 220 can be configured to process commands of a computer program by performing basic arithmetic, logic, and input / output operations. Commands can be provided to a user terminal (not shown) or another external system via memory 210 or communication module 230. For example, processor 220 can collect lifetime assessment data of a target battery from one or more manufacturing facilities, generate cumulative slip data based on the lifetime assessment data, calculate the correlation between cumulative slip and the performance lifetime of the target battery, and then predict the lifetime of the target battery based on that correlation.

[0079] The communication module 230 can provide the user terminal (not shown) and the information processing system 200 with configurations or functions for communicating with each other via a network, and can also provide the information processing system 200 with configurations or functions for communicating with external systems (for example, the manufacturing facility of the target battery, a separate cloud system, etc.). In some embodiments, control signals, commands, data, etc., provided under the control of the processor 220 of the information processing system 200 can be transmitted to the user terminal and / or the external system via the communication module 230 and the network through the communication module 230 and the communication module of the user terminal and / or the external system. For example, the predicted lifespan information of the target battery generated by the information processing system 200 can be transmitted to the user terminal and / or the external system via the communication module 230 and the network through the communication module 230 and the communication module of the user terminal and / or the external system. Furthermore, the user terminal and / or the external system that has received the predicted lifespan information of the target battery can output the received information via a device with display output functionality.

[0080] Furthermore, the input / output interface 240 of the information processing system 200 may be a means for interacting with a device (not shown) for input or output, which may be connected to the information processing system 200, or the information processing system 200 may include such a device. Figure 2In this diagram, the input / output interface 240 is shown as an element configured separately from the processor 220, but is not limited thereto, and the input / output interface 240 may be configured to be included within the processor 220. The information processing system 200 may include more than Figure 2 The components in the text are numerous. However, it is not necessary to clearly show most of the existing technology components.

[0081] The processor 220 of the information processing system 200 can be configured to manage, process, and / or store information and / or data received from multiple user terminals and / or multiple external systems. According to some embodiments, the processor 220 can receive target battery life assessment data, etc., from user terminals and / or external systems. The processor 220 can calculate cumulative slip based on the target battery life assessment data, predict the target battery life based on the calculated cumulative slip, and then output corresponding life prediction information, etc., via a device with display output functionality connected to the information processing system 200.

[0082] Figure 3 This is a block diagram illustrating a device 16 for predicting battery life according to some embodiments of the present disclosure.

[0083] refer to Figure 3 The device 16 for predicting battery life may include a data generation module 120, a slip life analysis module 140, and a life prediction module 160.

[0084] In some embodiments, the data generation module 120 can generate cumulative slip data based on the lifetime assessment data of the target battery. Here, the cumulative slip can be calculated based on cumulative capacity curve (CCP) data. The cumulative capacity curve (CCP) data can be data obtained by accumulating multiple charge capacity-voltage curves and multiple discharge capacity-voltage curves.

[0085] The specific configuration of the data generation module 120 will be provided in [the following text is incomplete and likely refers to a separate document or Figures 4 to 7B It is described in detail in the text.

[0086] In some embodiments, the slip lifetime analysis module 140 can calculate the correlation between cumulative slip and the degradation trend of the target battery. Here, the degradation trend can include the point of sudden drop in the target battery 12 and the point of EOL (end of life). The point of sudden drop can refer to the periodic point where the slope of the target battery's state of health (SOH) decreases rapidly due to rapid degradation of the target battery, etc. Based on the above correlation, the lifetime analysis module 140 can calculate the correlation between cumulative slip and the performance lifetime of the target battery.

[0087] In some embodiments, the slip lifetime analysis module 140 may use linear regression modeling techniques to calculate the correlation between cumulative slip and the performance lifetime of the target battery. However, this disclosure is not limited thereto. For example, the performance lifetime of the target battery may be included in the corresponding battery lifetime assessment data. Here, performance lifetime may refer to the life cycle when the state of health (SOH) of the target battery is between 85% and 75%. Preferably, performance lifetime may be, but is not limited to, the life cycle when the state of health (SOH) of the target battery is 80%. Here, the point where the state of health (SOH) of the target battery is 80% may be the end-of-life (EOL) point of the target battery.

[0088] In some embodiments, when the target battery's lifespan is in the range of 0 to 250 cycles, the cumulative slip can be calculated based on the cumulative charge slip and discharge slip. Preferably, the cumulative slip can be calculated in the range of 0 to 100 cycles of the target battery's lifespan. However, this disclosure is not limited thereto.

[0089] In some embodiments, the cumulative slip may be linear with respect to performance lifetime, and the slip analysis module 140 can calculate a correlation with such linearity. Therefore, the slip analysis module 140 can calculate the performance lifetime or health state corresponding to a specific cumulative slip of the target battery based on this linear correlation.

[0090] In some embodiments, the slip analysis module 140 may pre-calculate the correlation based on a linear regression model using cumulative slip and performance lifetime data for multiple batteries. The slip analysis module 140 may then store the pre-calculated correlation and, based on this, calculate the correlation between cumulative slip and the performance lifetime of the target battery. In some embodiments, the lifetime prediction module 160 may predict the state of health (SOH) of the target battery based on the correlation between cumulative slip and performance lifetime calculated by the lifetime analysis module 140, according to charge-discharge cycles.

[0091] As described above, the battery life assessment system 1 according to some embodiments of the present disclosure can easily predict the long-term life of the battery based on the cumulative slip of the initial life data of the battery.

[0092] Figure 4 This is a block diagram describing the configuration of a data generation module 120 according to some embodiments of the present disclosure. Figure 5 and Figure 6 This is an example describing the operation of the CCP calculation module 122 according to some embodiments of the present disclosure, and Figure 7A and Figure 7B This is an example describing the operation of the slip calculation module 124.

[0093] refer to Figure 4The data generation module 120 may include a CCP calculation module 122 and a slip calculation module 124.

[0094] In some embodiments, the CCP calculation module 122 can calculate cumulative capacity curve (CCP) data based on lifetime assessment data. For example, refer to Figure 5 It can measure multiple charging and discharging curves, which show the changes in charging and discharging voltages according to the changes in the charging capacity of the target battery. Each of the multiple charging curves can be a voltage curve corresponding to the capacity of the target battery within one charging cycle. Similarly, each of the multiple discharging curves can be a voltage curve corresponding to the capacity of the target battery within one discharging cycle.

[0095] refer to Figure 6 The CCP calculation module 122 can calculate the cumulative capacity curve (CCP) data based on multiple charge curves and multiple discharge curves extracted from the lifetime assessment data. Furthermore, the slip calculation module 124 can calculate the cumulative slip based on the cumulative capacity curve (CCP) data.

[0096] In some embodiments, the slip calculation module 124 can calculate multiple charge slips and multiple discharge slips based on cumulative capacity curve (CCP) data. (Reference) Figure 7A and Figure 7B Each of the multiple charging shifts can be the amount of change between the charging curve of the first charging cycle and the charging curve of a subsequent cycle of the first charging cycle. That is, the i-th charging shift ΔC_i can be the amount of change between the (i+1)-th charging curve C_i+1 and the i-th charging curve C_i. Furthermore, each of the multiple discharging shifts can be the amount of change between the discharging curve of the first discharging cycle and the discharging curve of a subsequent cycle of the first discharging cycle. That is, the i-th discharging shift ΔD_i can be the amount of change between the (i+1)-th discharging curve D_i+1 and the i-th discharging curve D_i.

[0097] The slip calculation module 124 can calculate the cumulative slip based on multiple charging slips ΔC and multiple discharging slips ΔD. Here, the cumulative slip can be the sum of the differences ΔC-ΔD between each of the multiple charging slips ΔC and the corresponding one of the multiple discharging slips ΔD. That is, the cumulative slip can be expressed as...

[0098] In some embodiments, the slip calculation module 124 may utilize lifetime assessment data when the target battery's lifetime cycles are in the range of 0 to 250. Therefore, the slip calculation module 124 may calculate the cumulative slip over the lifetime cycles in the range of 0 to 250.

[0099] Figure 8 and Figure 9 This is an example describing the operation of the slip lifetime analysis module 140 according to some embodiments of the present disclosure.

[0100] refer to Figure 8 The data generation module 120 can calculate the cumulative slip based on the predetermined lifespan of the target battery. Here, the predetermined lifespan can be 70 to 250 cycles, which is the range of the initial lifespan of the target battery. In this case, the cumulative slip can be calculated based on the initial lifespan data of the target battery. Figure 8 Multiple Designs of Experiments (DOEs) are shown, and each DOE represents a cumulative slip calculated from lifetime assessment data for different experimental batteries within a lifetime range of 70 to 250 cycles. The data generation module 120 can transmit the cumulative slip calculated therefrom to the lifetime analysis module 140. Furthermore, the lifetime analysis module 140 can receive lifetime assessment data and extract performance lifetime from the lifetime assessment data. Here, performance lifetime can be the lifetime cycles at which the state of health (SOH) of the target battery is 80%. Furthermore, the point where the target battery's SOH is 80% can be the end-of-life (EOL) point of the target battery.

[0101] refer to Figure 9 The life analysis module 140 can calculate the correlation between cumulative slip and performance life. Figure 9 The correlation calculated by the lifetime analysis module 140 based on the linear relationship between cumulative slip and performance lifetime calculated from multiple experimental data DOE1 to DOE6 is shown. Here, performance lifetime can be the life cycle when the state of health (SOH) is 80%. However, performance lifetime is merely an example and is not limited thereto. The lifetime analysis module 140 can then transmit the calculated correlation between cumulative slip and performance lifetime to the lifetime prediction module 160. The lifetime prediction module 160 can then predict the state of health (SOH) based on the charge-discharge cycles of the target battery, according to the correlation between cumulative slip and performance lifetime calculated by the lifetime analysis module 140.

[0102] Figure 10 This is a flowchart describing a method 1000 for predicting battery life according to some embodiments.

[0103] refer to Figure 10 The method 1000 for predicting battery life can be derived from... Figure 3 The device 16, used for predicting battery life, performs this function. Therefore, for ease of description, details already provided will be omitted. Figure 3 The details described in the text are repeated.

[0104] Method 1000 for predicting battery life may begin by calculating cumulative slip data based on life assessment data of the target battery (step 1010). For example, Figure 3 The data generation module 120 can calculate cumulative slip data based on the target battery's lifetime assessment data. Here, the lifetime assessment data may include voltage curve data regarding the target battery's capacity over a range of 0 to 250 charge-discharge cycles.

[0105] According to some embodiments, calculating cumulative slip data (step 1010) may include calculating cumulative capacity curve (CCP) data based on lifetime assessment data, and calculating cumulative slip based on the cumulative capacity curve (CCP) data. Further, calculating the cumulative capacity curve (CCP) data may include calculating the cumulative capacity curve (CCP) data based on multiple charging curves and multiple discharging curves extracted from the lifetime assessment data. Additionally, calculating cumulative slip may include calculating multiple charging slips and multiple discharging slips based on the cumulative capacity curve (CCP) data, and calculating the cumulative slip based on the multiple charging slips and multiple discharging slips.

[0106] Then, the correlation between cumulative slip and the performance lifetime of the target battery can be calculated (step 1020). For example, Figure 3 The lifetime analysis module 140 can calculate the correlation between cumulative slip and performance lifetime.

[0107] According to some embodiments, calculating the correlation (step 1020) may include calculating the correlation between cumulative slip and the degradation trend of the target battery. In other embodiments, calculating the correlation (step 1020) may include using a linear regression model technique to calculate the correlation between cumulative slip and the performance lifetime of the target battery.

[0108] Therefore, the lifespan of the target battery can be predicted based on correlation (step 1030). For example, Figure 3 The lifetime prediction module 160 can predict the lifetime of the target battery based on calculated correlations.

[0109] According to some embodiments, predicting the lifespan of the target battery (step 1030) may include predicting the state of health (SOH) of the target battery based on cumulative slip and charge-discharge cycles.

[0110] Although this disclosure has been described with reference to the accompanying drawings of various aspects of the embodiments and illustrated embodiments, this disclosure is not limited thereto. Various modifications and variations can be made by those skilled in the art within the scope of the technical spirit of this disclosure and the claims and their equivalents.

Claims

1. A method for predicting battery life, comprising: Cumulative slip data is calculated based on the target battery's lifetime assessment data; Calculate the correlation between cumulative slip and the performance lifetime of the target battery; as well as The lifespan of the target battery is predicted based on the correlation.

2. The method according to claim 1, wherein, The lifespan assessment data includes voltage curve data regarding the capacity of the target battery over a range of 0 to 250 charge-discharge cycles.

3. The method according to claim 2, wherein, The calculation of the cumulative slip data includes: Calculate cumulative capacity curve data based on the aforementioned lifetime assessment data; and The cumulative slip is calculated based on the cumulative capacity curve data.

4. The method according to claim 3, wherein, The calculation of the cumulative capacity curve data includes: The cumulative capacity curve data is calculated based on multiple charging curves and multiple discharging curves extracted from the lifetime assessment data.

5. The method according to claim 4, wherein, Each of the multiple charging curves is a voltage curve corresponding to the capacity of the target battery in a single charging cycle, and Each of the multiple discharge curves is a voltage curve corresponding to the capacity of the target battery in a single discharge cycle.

6. The method according to claim 5, wherein, Calculating the cumulative slip includes: Based on the cumulative capacity curve data, calculate multiple charging slip and multiple discharging slip; and The cumulative slip is calculated based on the plurality of charging slips and the plurality of discharging slips.

7. The method according to claim 6, wherein, The cumulative slip is the sum of the differences between each of the plurality of charging slips and the corresponding discharge slip among the plurality of discharging slips.

8. The method according to claim 7, wherein, Each of the plurality of charging slips is the amount of change between the charging curve of the first charging cycle and the charging curve of a subsequent cycle of the first charging cycle, and Each of the plurality of discharge slips is the amount of change between the discharge curve of the first discharge cycle and the discharge curve of a subsequent cycle of the first discharge cycle.

9. The method according to claim 8, wherein, Calculating the correlation between the cumulative slip and the performance lifetime of the target battery includes: Calculate the correlation between the cumulative slip and the degradation trend of the target battery.

10. The method according to claim 9, wherein, Calculating the correlation between the cumulative slip and the performance lifetime of the target battery includes: The correlation between the cumulative slip and the performance lifetime of the target battery is calculated using a linear regression model.

11. The method according to claim 10, wherein, Predicting the lifespan of the target battery includes: Based on the correlation between the cumulative slip and the performance life of the target battery, the health status of the target battery is predicted according to the charge-discharge cycle.

12. A device for predicting battery life, comprising: The data generation module is configured to generate cumulative slip data based on the target battery's life assessment data; The slip lifetime analysis module is configured to calculate the correlation between cumulative slip and the performance lifetime of the target battery; as well as A lifespan prediction module is configured to predict the lifespan of the target battery based on the correlation.

13. The device according to claim 12, wherein, The lifespan assessment data includes voltage curve data regarding the capacity of the target battery over a range of 0 to 250 charge-discharge cycles.

14. The device according to claim 13, wherein, The data generation module includes: The cumulative capacity curve calculation module is configured to calculate cumulative capacity curve data based on the lifetime assessment data; and The slip calculation module is configured to calculate the cumulative slip based on the cumulative capacity curve data.

15. The device according to claim 14, wherein, The cumulative capacity curve calculation module is configured to calculate the cumulative capacity curve data based on multiple charging curves and multiple discharging curves extracted from the lifetime assessment data.

16. The device according to claim 15, wherein, The slip calculation module is configured to calculate multiple charging slips and multiple discharging slips based on the cumulative capacity curve data.

17. The device according to claim 16, wherein, The slip calculation module is configured to calculate the cumulative slip based on the plurality of charging slips and the plurality of discharging slips.

18. The device according to claim 17, wherein, The slip lifetime analysis module is configured to calculate the correlation between the cumulative slip and the degradation trend of the target battery.

19. The device according to claim 18, wherein, The slip lifetime analysis module is configured to use linear regression modeling techniques to calculate the correlation between the cumulative slip and the performance lifetime of the target battery.

20. The device according to claim 19, wherein, The lifetime prediction module is configured to predict the health status of the target battery based on the correlation between the cumulative slip and the performance lifetime of the target battery, according to the charge-discharge cycle.

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