Battery life estimation method and battery system

Abstract

The invention relates to a battery life estimation method and a battery system. The battery system includes: a battery; a detection device for detecting a voltage, a current and a temperature from the battery; and a battery management system (BMS) for: estimating a state of charge (SOC) based on the voltage, current, and temperature detected from the detection means; storing a configuration file generated by measuring a physical state of the battery in an event count format based on battery state data including voltage, current, temperature, and SOC; generating a virtual scene for driving the representative battery based on the configuration file; and estimating the life of the battery through the virtual scene.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2023-0086671 filed in the Korean Intellectual Property Office on July 4, 2023, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] Aspects of embodiments of the present disclosure relate to a battery life estimation method and a battery system. Background Art

[0004] The battery life estimation model configures various combinations of various factors that affect battery degradation, and based on such data, an expected life value for a battery usage profile can be obtained.

[0005] However, the lifetime degradation of a battery is not determined solely by the physical conditions at the corresponding time, but has a hysteresis characteristic. For example, the reaction mechanism inside a battery operated at room temperature for the first 6 months of a 1-year cycle and at a low temperature of 10 degrees for the next 6 months may be different from the reaction mechanism inside a battery operated at low temperature for the first 6 months of a 1-year cycle and at room temperature for the next 6 months. Therefore, information about the time sequence of operating batteries may be an important (e.g., very important) consideration.

[0006] The above information disclosed in this Background section is for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute prior art. Summary of the invention

[0007] Generally, due to limited storage capacity, an energy storage system (ESS) or a BMS system of a vehicle may have difficulty storing factors affecting life degradation in chronological order, and data may be stored in a format that counts events of changes in each factor over time.

[0008] However, since data stored in an event count format may not include information that changes over time and may be data that simply counts physical quantities, estimating a battery life using only such corresponding information may be inaccurate.

[0009] One or more embodiments of the present disclosure are directed to a battery system and a battery life estimation method that can estimate the battery life more accurately.

[0010] According to one or more embodiments of the present disclosure, a battery system includes: a battery; a detection device configured to detect voltage, current and temperature from the battery; and a battery management system (BMS) configured to: estimate a state of charge (SOC) based on the voltage, current and temperature detected from the detection device; store a configuration file generated by measuring a physical state of the battery in an event count format based on battery status data including voltage, current, temperature and SOC; generate a virtual scenario for driving a representative battery based on the configuration file; and estimate the life of the battery through the virtual scenario.

[0011] In an embodiment, the BMS may include: a scenario generator configured to generate a scenario of M days, where M is a natural number greater than or equal to 1; a scenario corrector configured to generate N scenarios of M days based on the scenario of M days, where N is a natural number greater than or equal to 2; and a life estimator configured to estimate the life of the battery by simulating battery operation based on the N scenarios of M days.

[0012] In an embodiment, the scenario generator may be configured to generate a scenario template for M days based on the probability of occurrence of each of a plurality of stored representative templates; and each of the plurality of stored representative templates may include a value representing a charge and discharge ratio of the battery in days as a probability.

[0013] In an embodiment, the scenario generator may be configured to randomly sample physical quantities at each time point when the charge state of a representative battery is switched in the scenario template for M days from a configuration file to generate a specific scenario for M days.

[0014] In an embodiment, the configuration file may include: a charging log table, generated based on data obtained from the battery status data when the battery is in a charging state; a discharging log table, generated based on data obtained from the battery status data when the battery is in a discharging state; and a rest log table, generated based on data obtained from the battery status data when the battery is in a resting state.

[0015] In an embodiment, the scenario generator can be configured to: export the physical quantities at the starting time point of the charging period of the M-day scenario template from the charging log table; export the physical quantities at the starting time point of the discharging period of the M-day scenario template from the discharging log table; and export the physical quantities at the starting time point of the rest period of the M-day scenario template from the rest log table.

[0016] In an embodiment, the scene corrector may be configured to restore N number of scenes among the scenes of M days by using a Monte Carlo simulation method to generate N scenes of M days.

[0017] In an embodiment, the life estimator may be configured to: simulate N scenarios of M days multiple times; determine one simulation result among the multiple simulation results as the battery life estimation model; and estimate the battery life using the service life estimation model.

[0018] According to one or more embodiments of the present disclosure, a battery life estimation method includes: detecting voltage, current, and temperature from a battery; estimating a state of charge (SOC) based on the voltage, current, and temperature detected from the battery; generating a profile by measuring a physical state of the battery in an event count format based on battery status data including voltage, current, temperature, and SOC, and storing the profile; generating a virtual scene for driving a representative battery based on the profile; and estimating the life of the battery through the virtual scene.

[0019] In an embodiment, the estimation of the battery life may include: generating an M-day scenario, where M is a natural number greater than or equal to 1; generating N scenarios of M days based on the M-day scenario, where N is a natural number greater than or equal to 2; and estimating the battery life by simulating battery operation based on the N scenarios of M days.

[0020] In an embodiment, generation of an M-day scenario may include generating an M-day scenario template based on the probability of occurrence of each of a plurality of stored representative templates, and each of the plurality of stored representative templates may include representing the charge and discharge ratio of the battery in days as a value of probability.

[0021] In an embodiment, the generation of the M-day scenario may include: based on each time point of switching the charge state of the representative battery in the M-day scenario template, randomly sampling the physical quantity at each time point from the configuration file to generate a specific scenario for the M-day.

[0022] In an embodiment, generation of a configuration file may include: generating a charging log table based on data obtained from battery status data when the battery is in a charging state; generating a discharging log table based on data obtained from battery status data when the battery is in a discharging state; and generating a rest log table based on data obtained from battery status data when the battery is in a resting state.

[0023] In an embodiment, the generation of a specific scenario for M days may include: deriving the physical quantity at the starting time point of the charging period of the scenario template for M days from the charging log table; deriving the physical quantity at the starting time point of the discharging period of the scenario template for M days from the discharging log table; and deriving the physical quantity at the starting time point of the rest period of the scenario template for M days from the rest log table.

[0024] In an embodiment, the generating of N scenarios for M days may include: restoring N number of scenarios among the scenarios for M days by using a Monte Carlo simulation method to generate N scenarios for M days.

[0025] In an embodiment, estimating the battery life by simulating battery operation may include: simulating N scenarios of M days multiple times; determining one simulation result among the simulation results of the multiple simulations as a battery life estimation model; and estimating the battery life using a service life estimation model.

[0026] According to one or more embodiments of the present disclosure, the life of a battery module may be estimated with improved accuracy (eg, high accuracy) using a memory of smaller capacity (eg, small capacity).

[0027] According to one or more embodiments of the present disclosure, warranty risks may be detected in advance through accurate lifetime estimation of a battery.

[0028] According to one or more embodiments of the present disclosure, if a life problem occurs in the future, the user's battery usage history can be estimated, and thus, a standard for determining whether the battery has a problem or is abnormally used outside the warranty range can be prepared. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other aspects and features of the present disclosure will be more clearly understood from the following detailed description of illustrative, non-limiting embodiments with reference to the accompanying drawings.

[0030] Figure 1 A block diagram of a battery system according to an embodiment is illustrated.

[0031] Figure 2 An example of a rest log table according to an embodiment is illustrated.

[0032] Figure 3 Graphics Figure 1 Block diagram of the MCU configuration.

[0033] Figure 4 A flow chart of a battery life estimation method according to an embodiment is shown.

[0034] Figure 5 An example of a representative scene template according to an embodiment is illustrated.

[0035] Figure 6 An example of a scenario template for M days according to an embodiment is illustrated.

[0036] Figure 7 An example of the operation of generating an M-day scenario according to an embodiment is illustrated.

[0037] Figure 8An example of a best case scenario among various scenarios according to an embodiment is illustrated.

[0038] Fig. 9 An example of a worst-case scenario among various scenarios according to an embodiment is illustrated.

[0039] Fig.10 A graph illustrating simulation results obtained by simulating a reconstructed scene 3000 times according to an embodiment.

[0040] Fig.11 Graphics Fig.10 The simulation results are shown in a graph of the averaged data.

[0041] Fig.12 The icon will Fig.11 Shown is a graph comparing the average data with the values ​​estimated by a model of the actual life of the battery.

[0042] Description of Reference Numerals

[0043] 1: Battery system

[0044] 10: Battery module

[0045] 20: Detection device

[0046] 21: Voltage detector

[0047] 22: Current detector

[0048] 23: Temperature detector

[0049] 30: Battery Management System (BMS)

[0050] 31: SOC Estimator

[0051] 32: Configuration file generator

[0052] 33: Main Control Unit (MCU)

[0053] 331: Scene Generator

[0054] 332: Scene Corrector

[0055] 333: Lifespan Estimator

[0056] 40: Connection part

[0057] 2: External device DETAILED DESCRIPTION

[0058] Hereinafter, the embodiments will be described in more detail with reference to the accompanying drawings, in which the same reference numerals always refer to the same elements. However, the present disclosure may be embodied in various different forms and should not be construed as being limited to the embodiments illustrated herein. On the contrary, these embodiments are provided as examples so that the present disclosure will be thorough and complete, and will fully convey the aspects and features of the present disclosure to those skilled in the art. Accordingly, processes, elements and techniques that are not necessary for a person of ordinary skill in the art to fully understand the aspects and features of the present disclosure may not be described. Unless otherwise stated, throughout the drawings and written descriptions, the same reference numerals represent the same elements, and therefore, their redundant descriptions may not be repeated.

[0059] When a certain embodiment can be implemented differently, the specific process order may be different from the described order. For example, two processes described in succession may be performed simultaneously or substantially simultaneously, or may be performed in the order opposite to the described order.

[0060] 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 sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms are used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Therefore, without departing from the spirit and scope of the present disclosure, the first element, component, region, layer or section described below may be referred to as a second element, component, region, layer or section.

[0061] 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 may be directly on, connected to, or coupled to the other element or layer, or there may be one or more intervening elements or layers. Similarly, when a layer, region, or element is referred to as being "electrically connected to" another layer, region, or element, it may be directly electrically connected to the other layer, region, or element, and / or may be indirectly electrically connected to the other layer, region, or element with one or more intervening layers, regions, or elements therebetween. Additionally, it will be understood that when an element or layer is referred to as being "between" two elements or layers, it may be the only element or layer between the two elements or layers, or there may also be one or more intervening elements or layers.

[0062] The terms used in this article are for the purpose of describing a specific embodiment, rather than being intended to limit the present disclosure. As used in this article, the singular form "one" is also intended to include plural forms, unless the context clearly indicates otherwise. It will be further understood that when used in this specification, the terms "include", "comprise", "have" and their variants specify the existence of stated features, integers, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, integers, steps, operations, elements, parts and / or their groups. As used in this article, the term "and / or" includes any and all combinations of one or more of the associated listed items. For example, the statement "A and / or B" means A, B or A and B. When after the element list, the statement such as "at least one of ..." modifies the entire element list, without modifying the single element of the list. For example, the statement "at least one of a, b and c" and "at least one selected from the group consisting of a, b and c" indicate only a, only b, only c, both a and b, both a and c, both b and c, all of a, b and c or their variants.

[0063] As used herein, the terms "substantially," "about," and similar terms are used as terms of approximation rather than as terms of degree, and are intended to take into account the inherent variation in measurements or calculations that would be recognized by one of ordinary skill in the art. Further, when describing embodiments of the present disclosure, the use of "may" refers to "one or more embodiments of the present disclosure." As used herein, the terms "use," "using," and "used" may be considered synonymous with the terms "utilize," "utilizing," and "utilized," respectively.

[0064] As used herein, the terms "module", "unit", "part" or "component" used in the following description to represent constituent elements are used only to make the specification easier to understand, and therefore, these terms themselves do not have a meaning or function to distinguish them from each other. According to an embodiment, a program implemented as a set of instructions embodying a control algorithm for controlling another component can be installed in a component among the components for controlling another component under specific control conditions. The control component can generate output data by processing input data and storing data according to the installed program. The control component may include a non-volatile memory for storing the program and a memory for storing data.

[0065] In addition, any suitable hardware, firmware (e.g., application specific integrated circuit), software, or a combination of software, firmware, and hardware may be utilized to implement the electronic or electrical device and / or any other related device or component according to the embodiments of the present disclosure described herein. For example, the various components of these devices may be formed on an integrated circuit (IC) chip or on separate IC chips. Further, the various components of these devices may be implemented on a flexible printed circuit film, a tape carrier package (TCP), a printed circuit board (PCB), or formed on a substrate. Further, the various components of these devices may be processes or threads running on one or more processors in one or more computing devices, which execute computer program instructions and interact with other system components for performing the various functions described herein. The computer program instructions are stored in a memory, which may be implemented in a computing device using a standard memory device (such as, for example, a random access memory (RAM)). The computer program instructions may also be stored in other non-temporary computer-readable media (such as, for example, a CD-ROM or a flash drive, etc.). Moreover, those skilled in the art will recognize that the functionality of various computing devices may be combined or integrated into a single computing device, or the functionality of a particular computing device may be distributed across one or more other computing devices without departing from the spirit and scope of the example embodiments of the present disclosure.

[0066] Unless otherwise defined, all terms (including technical and scientific terms) used in this article have the same meaning as those commonly understood by ordinary technicians in the field to which the present disclosure belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant field and / or this specification, and should not be interpreted in an idealized or overly formal sense unless explicitly defined in this article.

[0067] Figure 1 A block diagram of a battery system according to an embodiment is illustrated.

[0068] refer to Figure 1 , the battery system 1 may include a battery module (eg, battery) 10 , a detection device 20 , a battery management system (BMS) 30 , and a connection portion 40 .

[0069] The connection portion 40 may be implemented as a relay or the like, and may be connected in series between the battery module 10 and the external device 2. The connection portion 40 may be operated by a signal supplied from the BMS 30.

[0070] The battery system 1 may be connected to an external device 2. The external device 2 may include a load and / or a charging device such as an inverter and a converter. For example, the battery system 1 may be connected to a charger, and power may be supplied from the charger for charging. As another example, the battery system 1 may be connected to a load, and power supplied by the battery module 10 may be discharged through the load.

[0071] The battery module 10 may include a plurality of battery cells connected in series and / or in parallel.

[0072] The detection device 20 may detect the state of the cells constituting the battery module 10 based on the signals received from the battery module 10. The signals received by the detection device 20 from the battery module 10 may include a voltage measurement signal indicating a voltage across each of the cells constituting the battery module 10, a current measurement signal indicating a current flowing through the battery module 10, and a temperature measurement signal indicating a temperature.

[0073] The detection device 20 may include a voltage detector 21 , a current detector 22 , and a temperature detector 23 .

[0074] The voltage detector 21 may detect a cell voltage of each of the cells constituting the battery module 10 and / or a module voltage of the battery module 10, etc. The current detector 22 may detect a current flowing through the battery module 10. The temperature detector 23 may detect a temperature from at least one point of the battery module 10.

[0075] Hereinafter, the voltage information, current information, and temperature information generated by the detection device 20 may be referred to as detection data. If an event (e.g., a preset event) occurs, detection data may be generated. For example, the preset event may include the arrival of a time according to a cycle (e.g., a predetermined cycle), switching to a charging state, a discharging state, or a resting state of the battery, the start and end of charging of the battery, and / or the time (e.g., a predetermined time) elapsed after starting battery charging. Hereinafter, for convenience, it is assumed that if an event occurs in which the detection device 20 switches to each of the charging state, the discharging state, and the resting state of the battery module 10, detection data is collected.

[0076] The BMS 30 may receive detection data of the battery module 10 from the detection device 20. The BMS 30 may monitor the voltage, current, temperature, state of charge (SOC) and / or charge rate (C-rate), etc. (hereinafter, referred to as "battery state") of the battery module 10 based on the detected data. The BMS 30 may generate a battery charge / discharge profile based on the battery state monitoring result, and may estimate the life of the battery module 10 based on the battery charge / discharge profile.

[0077] The BMS 30 may include an SOC estimator 31 , a profile generator 32 , and a main control unit (MCU) (eg, main controller) 33 .

[0078] The SOC estimator 31 can estimate the SOC of the battery module 10 using one of a current integration method that integrates the charge / discharge current, an electrochemical model method that represents the chemical reactions inside the cell in molecules, a mathematical method that represents the dynamic behavior of the battery's operating time and state of charge (SOC) in a purely mathematical experimental manner, and / or a voltage modeling method that uses the relationship between open circuit voltage (OCV) and state of charge (SOC).

[0079] The SOC estimator 31 may transmit a value estimated as the SOC of the battery module 10 (hereinafter, referred to as “SOC of the battery module 10 ”) to the profile generator 32 and the MCU 33 .

[0080] Hereinafter, the battery state data may indicate a battery state monitored by the BMS 30 , such as detection data obtained from the detection device 20 by the profile generator 32 and the SOC and C-rate of the battery module 10 obtained from the SOC estimator 31 .

[0081] The profile generator 32 may generate and store a profile (hereinafter referred to as a "profile") representing physical values ​​of factors related to the life of the battery module 10 based on the battery status data. Here, the profile is a time series profile representing how various factors (such as SOC, C-rate, and temperature) change over time, and may be classified according to the state of charge of the battery. The state of charge of the battery may be one of a charging state, a discharging state, and a resting state. Here, the discharging state may include a state in which the vehicle is traveling (driving), and the resting state may include a state in which the vehicle is parked (parking).

[0082] The profile generator 32 may indicate the physical state of the battery module 10, and may store a profile generated by measuring events of factors related to the life of the battery module 10 in an event count format. The event count format may represent a method of measuring a physical quantity (e.g., an event) to be stored in a corresponding storage at regular time intervals. Hereinafter, the profile generated by the profile generator 32 by measuring factors related to the life of the battery module 10 in an event count format is referred to as field data.

[0083] The battery status data indicates the values ​​of factors (such as SOC, temperature, and C-rate) changing over time, and because the field data is generated and stored in an event count format, it can represent the values ​​of each factor (SOC, temperature, and C-rate) in addition to the physical quantity of time.

[0084] The field data may be represented by a three-dimensional matrix of physical quantities representing SOC, temperature, and C-rate. For example, the field data may be a log table representing events related to the life of the battery module 10 for each state of charge of the battery in a matrix of three physical quantities representing SOC, temperature, and C-rate.

[0085] The profile generator 32 may implement the field data generated based on the battery status data obtained when the battery module 10 is in a charging state as a charging log table. The profile generator 32 may implement the field data generated based on the battery status data obtained when the battery module 10 is in a discharging state as a discharging log table. The profile generator 32 may implement the field data generated based on the battery status data obtained when the battery module 10 is in a resting state as a resting log table.

[0086] Hereinafter, for the sake of convenience, it is assumed that the field data generated by the profile generator 32 is at least one of a charging log table, a discharging log table, and a rest log table.

[0087] Figure 2 An example of a rest log table according to an embodiment is illustrated.

[0088] refer to Figure 2 If the battery module 10 is in a rest state, the profile generator 32 may generate a log table by counting events of SOC and temperature values. Figure 2 In the example of , if the SOC is greater than 0 and less than 5 and the temperature is greater than 20 degrees and less than 25 degrees, most of the events are recorded.

[0089] However, the present disclosure is not limited thereto, and the charging log table, the discharging log table, and the rest log table may be stored in the BMS 30 .

[0090] Hereinafter, for the sake of convenience, it is assumed that the profile generator 32 generates and stores a charging log table, a discharging log table, and a rest log table.

[0091] The MCU 33 may generate a scenario template for driving the battery module 10 based on the battery status data. The MCU 33 may generate a virtual scenario (hereinafter, referred to as a "scenario") for driving the battery based on the scenario template and the field data generated by the profile generator 32, and may estimate the life of the battery module 10 by simulating the battery operation based on the generated scenario.

[0092] Figure 3 Graphics Figure 1 Block diagram of the MCU configuration.

[0093] refer to Figure 3, the MCU 33 may include a scene generator 331 , a scene corrector 332 , and a life estimator 333 .

[0094] Based on the battery status data, the scenario generator 331 may generate an M-day scenario (wherein M is a natural number of 1 or greater) in which the battery module 10 is driven for M days.

[0095] The scene corrector 332 may correct the M-day scene generated by the scene generator 331 to generate N (where N is a natural number equal to or greater than 2) M-day scenes to improve scene accuracy.

[0096] The life estimator 333 may simulate driving of the battery based on the corrected scenario, and may estimate the life of the battery module 10 through the simulation.

[0097] In the following, reference will be made to Figure 4 The operation of the MCU 33 is described in more detail.

[0098] Figure 4 A flow chart of a battery life estimation method according to an embodiment is shown.

[0099] Hereinafter, redundant description with respect to the above-described battery system 1 may not be repeated.

[0100] refer to Figure 4 , the detection device 20 may collect detection data based on the signal received from the battery module 10 within a suitable period (eg, a predetermined period), and the SOC estimator 31 may estimate the SOC of the battery module 10 based on the detection data (step S101).

[0101] The profile generator 32 may generate a log table representing the profile by counting events representing physical values ​​of factors related to the life of the battery module 10 based on the battery status data (step S102 ).

[0102] The BMS 30 may pre-store the occurrence probability of each of a plurality of representative templates represented by the battery state data.

[0103] In the following, reference will be made to Figure 5 The occurrence probability of each of the plurality of representative templates is described in more detail.

[0104] Figure 5 An example of a representative scene template according to an embodiment is illustrated.

[0105] A plurality of templates may represent the pattern of a scenario. Here, if the actual usage pattern of the battery module 10 is indicated by the ratio of charging, discharging, and resting every day and the SOC value changing over time, the pattern of the scenario may be represented by the shape of a curve that appears regularly. A plurality of templates may be generated in units of days according to the actual usage of the battery module 10.

[0106] Among the plurality of templates, a plurality of representative templates representing the actual usage pattern of the battery module 10 may be stored (e.g., pre-stored) in the BMS 30. The plurality of representative templates may be templates having an occurrence probability greater than or equal to a level (e.g., a predetermined level) among the plurality of templates, or a number (e.g., a predetermined number) of templates among the plurality of templates.

[0107] refer to Figure 5 , if the battery module 10 is an energy storage system (ESS), the representative templates are classified into solar energy, peak shaving, wind power, etc. according to the application of the energy stored in the ESS. Each representative template may include a value representing the daily charge and discharge (or charge, discharge, and rest) ratio of each classified application as a probability, and a daily graph according to the daily charge and discharge (or charge, discharge, and rest) ratio. For example, the BMS 30 may receive the probability of the daily charge and discharge ratio analyzed from the information of the solar energy application that has been used as charge 1 / discharge 1-87.1%, charge 2 / discharge 1-9.2%, and charge 3 / discharge 1-3.7%.

[0108] exist Figure 5 In the example of , the solar energy application may include: a first representative template including 1 charging, 1 rest, and 1 discharging per day, a second representative template including 2 charging, 2 rests, and 1 discharging per day, and a third representative template including 3 charging, 3 rests, and 1 discharging per day.

[0109] In addition, the peak shaving application may include: a fourth representative template including 1 charge, 1 rest, and 1 discharge per day, a fifth representative template including 1 charge, 2 rests, and 2 discharges per day, and a sixth representative template including 1 charge, 3 rests, and 3 discharges per day.

[0110] In addition, wind power applications may include: a seventh representative template including 1 charge, 1 rest, and 1 discharge per day, an eighth representative template including 2 charges, 2 rests, and 1 discharge per day, a ninth representative template including 3 charges, 3 rests, and 1 discharge per day, a tenth representative template including 4 charges, 4 rests, and 1 discharge per day, and an eleventh representative template including 1 charge, 2 rests, and 2 discharges per day.

[0111] In solar energy applications, it was found that the probability of occurrence of the first representative template including 1 charge, 1 rest, and 1 discharge per day was 87.1% of the actual usage pattern of the entire solar energy application. In addition, it was found that the probability of occurrence of the second representative template including 2 charges, 2 rests, and 1 discharge per day was 9.2% of the actual usage pattern of the entire solar energy application. In addition, it was found that the probability of occurrence of the third representative template including 3 charges, 3 rests, and 1 discharge per day was 3.7% of the actual usage pattern of the entire solar energy application.

[0112] In the peak shaving application, it was found that the probability of occurrence of the fourth representative template including 1 charge, 1 rest and 1 discharge per day was 43.0% of the actual usage pattern of the entire peak shaving application. In addition, it was found that the probability of occurrence of the fifth representative template including 1 charge, 2 rests and 2 discharges per day was 49.1% of the actual usage pattern of the entire peak shaving application. In addition, it was found that the probability of occurrence of the sixth representative template including 1 charge, 3 rests and 3 discharges per day was 7.9% of the actual usage pattern of the entire peak shaving application.

[0113] In wind power applications, it was found that the probability of occurrence of the seventh representative template including 1 charge, 1 rest and 1 discharge per day was 44.9% of the actual use pattern of the entire wind power application. In addition, it was found that the probability of occurrence of the eighth representative template including 2 charges, 2 rests and 1 discharge per day was 20.0% of the actual use pattern of the entire wind power application. In addition, it was found that the probability of occurrence of the ninth representative template including 3 charges, 3 rests and 1 discharge per day was 16.7% of the actual use pattern of the entire wind power application. In addition, it was found that the probability of occurrence of the tenth representative template including 4 charges, 4 rests and 1 discharge per day was 11.9% of the actual use pattern of the entire wind power application. In addition, it was found that the probability of occurrence of the eleventh representative template including 1 charge, 2 rests and 2 discharges per day was 6.4% of the actual use pattern of the entire wind power application.

[0114] Return to reference Figure 4 The scene generator 331 may configure (eg, may generate based on the occurrence probability of each of a plurality of pre-stored representative templates) a scene template for M days (step S103).

[0115] In the following, reference will be made to Figure 6 The configuration of the scene template (step S103) is described in more detail.

[0116] Figure 6 An example of a scenario template for M days according to an embodiment is illustrated.

[0117] The scene generator 331 can generate a scene template for a period (e.g., a predetermined period) to reconstruct the scene for the period using a representative template. Here, the period (e.g., a predetermined period) can be predetermined as initial information. In the following, the period is assumed to be M days.

[0118] The scene generator 331 may construct M-day scene templates by connecting M representative templates according to the occurrence probability of the representative templates.

[0119] exist Figure 6 In the example of , a solar energy application is illustrated. In the solar energy application, the scenario generator can generate a scenario template for M days, which includes: 87.1% of the first representative template including 1 charge and 1 discharge per day, 9.2% of the second representative template including 2 charges and 1 discharge per day, and 3.7% of the third representative template including 3 charges and 1 discharge per day.

[0120] refer to Figure 6 For example, a 100-day scenario template corresponding to a solar energy application generated by the scenario generator 331 may include: 87 first representative templates including 1 charging and 1 discharging per day, 9 second representative templates including 2 charging and 1 discharging per day, and 4 third representative templates including 3 charging and 1 discharging per day.

[0121] Return to reference Figure 4 , the scene generator 331 can generate a scene of M days that embodies the scene template of M days based on the log table (step S104).

[0122] In the following, reference will be made to Figure 7 The generation of the M-day scenario (eg, step S104 ) is described in more detail.

[0123] Figure 7 An example of the operation of generating an M-day scenario according to an embodiment is illustrated.

[0124] The M-day scenario template may include multiple time periods when the battery module 10 is charged (hereinafter, referred to as "charging periods"), multiple time periods when the battery module 10 is discharged (hereinafter, referred to as "discharging periods"), and multiple time periods when the battery module 10 is in a resting state (hereinafter, referred to as "resting periods"). The scenario generator 331 may implement the template by performing random sampling based on random numbers according to the probability distribution of the physical properties required at each time point of switching the charging state of the battery in the M-day scenario template. Each time point of switching the battery charging state may indicate the starting time point of each of the multiple charging intervals, multiple discharging intervals, and multiple resting intervals included in the M-day scenario template.

[0125] The scenario generator 331 may derive physical quantities (or in other words, values ​​such as C-rate, SOC, and temperature) required at each time point when the charge state of the battery is switched from one of the charge log table, the discharge log table, and the rest log table by random sampling.

[0126] For example, if the scenario generator 331 derives values ​​such as C-rate, SOC, and temperature from one of the charge log table, the discharge log table, and the rest log table, the value to be derived may be determined based on the number of events represented by each log table. Figure 7 In the example of , in the time period in which the C-rate representing the highest number of events (e.g., 56894) in the discharge log table is 0.35 or greater and less than 0.40 and the SOC is greater than 50 and less than 55, the scenario generator 331 can derive a C-rate value and an SOC value.

[0127] exist Figure 7 In the example, the scene template for M days may include a discharging period (driving #1), a rest period (parking #1-1), a charging period (charging #1), a rest period (parking #1-2), as well as a discharging period (driving #2), a rest period (parking #2-1), a charging period (charging #2), and a rest period (parking #2-2), etc.

[0128] refer to Figure 7 , the time point at which the discharging period (driving #1) starts is called t1, the time point at which the rest period (stop #1-1) starts is called t2, the time point at which the charging period (charging #1) starts is called t3, and the time point at which the rest period (stop #1-2) starts is called t4.

[0129] Since time point t1 is the start time point of the discharge period (travel #1), the scenario generator 331 can derive the C-rate value and the SOC value from the discharge log table. For example, the scenario generator 331 can derive a C-rate value within the range of 0.35 or more and less than 0.40 from the discharge log table, and can derive a SOC value within the range of greater than 50 and less than 55.

[0130] The scenario generator 331 can implement the scenario by using a C-rate value and an SOC value derived from the discharge log table. For example, the scenario generator 331 can set the SOC value derived from the discharge log table as the SOC value at time point t1, and can set the C-rate value derived from the discharge log table as the slope value of the SOC curve from time point t1 to time point t2.

[0131] Since time point t2 is the start time point of the rest period (Stop #1-1), the scenario generator 331 can derive the SOC value and the temperature value from the rest log table. For example, the scenario generator 331 can derive an SOC value within a range of greater than 35 and less than 40 from the rest log table, and can derive a temperature within a range of greater than 20 degrees and less than 25 degrees.

[0132] The scenario generator 331 can implement the scenario by using an SOC value and a temperature value derived from the rest log table. For example, the scenario generator 331 can set the SOC value derived from the rest log table as the SOC value at time point t2, and can set the temperature value derived from the rest log table as the temperature value from time point t2 to time point t3.

[0133] Since time point t3 is the start time point of the charging period (charging #1), the scenario generator 331 can derive the C-rate value and the SOC value from the charging log table. For example, the scenario generator 331 can derive a C-rate value within a range of 0.15 or more and less than 0.20 from the charging log table, and can derive an SOC value within a range of greater than 40 and less than 45.

[0134] The scenario generator 331 may implement the scenario by using a C-rate value and an SOC value derived from the charging log table. For example, the scenario generator 331 may set the SOC value derived from the charging log table as the SOC value at time point t3, and may set the C-rate value derived from the charging log table as the absolute value of the slope of the SOC curve from time point t3 to time point t4.

[0135] Since time point t4 is the start time point of the rest period (Stop #1-2), the scenario generator 331 can derive the SOC value and the temperature value from the rest log table. For example, the scenario generator 331 can derive an SOC value within a range of greater than 35 and less than 40 from the rest log table, and can derive a temperature within a range of greater than 20 degrees and less than 25 degrees.

[0136] The scenario generator 331 can implement the scenario by using an SOC value and a temperature value derived from the rest log table. For example, the scenario generator 331 can set the SOC value derived from the rest log table as the SOC value at time point t4, and can set the temperature value derived from the rest log table as the temperature value from time point t4 to time point t5.

[0137] As described above, the scenario generator 331 can generate a detailed scenario for M days based on the charging log table, the discharging log table, and the rest log table by randomly sampling physical values ​​corresponding to the start time points of each of the multiple charging periods, the multiple discharging periods, and the multiple rest periods included in the scenario template for M days.

[0138] Return to reference Figure 4 , the scene corrector 332 can correct and reconstruct the scene of M days (step S105).

[0139] The field data generated by the profile generator 32 may not include a physical quantity representing time in the process of analyzing the profile and analyzing events related to the life of the battery module 10. The scenario corrector 332 and the life estimator 333 may use a Monte Carlo simulation method to supplement the missing time information in the field data so that the M-day scenario can cover various situations.

[0140] The scene corrector 332 may restore N (where N is a natural number of 2 or more) scenes of the M-day scene. N may be a number sufficient to cover various situations in the scene.

[0141] In the following, reference will be made to Figure 8 and Fig. 9 The best case scenario and the worst case scenario among the N scenarios for M days generated by the scenario corrector 332 are described in more detail.

[0142] Figure 8 An example of a best case scenario among various scenarios according to an embodiment is illustrated.

[0143] refer to Figure 8 The best scenario among the N scenarios of M days restored by the scenario corrector 332 may be a scenario where low depth of discharge (DOD), low C-rate, and optimal temperature are combined with each other. Here, DOD may be estimated from SOC.

[0144] Fig. 9 An example of a worst-case scenario among various scenarios according to an embodiment is illustrated.

[0145] refer to Fig. 9 The worst case scenario among the N scenarios of M days restored by the scenario corrector 332 may be a situation where high DOD, high C-rate, and the worst temperature are combined with each other.

[0146] refer to Figure 8 and Fig. 9 The M-day scenario generated by the scenario generator 331 and the N scenarios of M days restored by the scenario corrector 332 are constructed by extracting physical quantities from field data, so there is no continuity of SOC, unlike the original pattern of SOC changing over time.

[0147] The scene corrector 332 may generate N scenes of M days including the best case scenario and the worst case scenario, and may reconstruct a scene including the N scenes of M days. Hereinafter, for convenience, the N scenes of M days generated and reconstructed by the scene corrector 332 are referred to as "reconstructed scenes".

[0148] Return to reference Figure 4 , the life estimator 333 may estimate the life of the battery module 10 by performing a simulation for estimating the battery life (hereinafter, referred to as “simulation”) on the reconstructed scenario (step S106 ).

[0149] The simulation can virtually perform charging and discharging of the battery according to the SOC value indicated by the scenario, and can estimate the SOH of the battery. The SOH of the battery can indicate the life span and degree of degradation of the corresponding battery.

[0150] In the following, reference will be made to Figures 10 to 12 The operation of the lifetime estimator 333 for correcting scenarios through simulation is described in more detail.

[0151] Fig.10 A graph illustrating simulation results obtained by simulating a reconstructed scene 3000 times according to an embodiment.

[0152] Fig.11 Graphics Fig.10 The simulation results are shown in a graph of the averaged data.

[0153] refer to Fig.10 , the life estimator 333 can be performed multiple times with respect to the reconstructed scene. Fig.10 and Fig.11 In the example of , it is shown that the lifetime estimator 333 has performed 3000 simulations.

[0154] refer to Fig.11 The life estimator 333 may represent average data among simulation results of scenarios reconstructed by multiple simulations in the curve graph.

[0155] exist Fig.11 , N scenarios of M days are simulated 3000 times, and the average data of the simulation results are representative values ​​of the 3000 simulations, but the present disclosure is not limited thereto.

[0156] The life estimator 333 may determine the life estimation model of the battery module 10 by using one data that best represents the scenario among the results of a plurality of simulations of N scenarios for M days as a representative value of the simulation.

[0157] Fig.12 The icon will Fig.11 Shown is a graph comparing the average data with the values ​​estimated by a model of the actual life of the battery.

[0158] refer to Fig.12 ,according to Fig.11 The average data (e.g., data log model average) of the embodiment shown in FIG. 1 is shown as a solid line, and the estimated value of the actual life model of the comparison battery (e.g., life model estimated value) is shown as a dotted line. Fig.12 By comparing the solid line and the dashed line shown in FIG. 1 , the accuracy of the scene reconstructed according to one or more embodiments can be confirmed.

[0159] Based on Fig.11 Using the average data of the embodiment shown in FIG. 1 , the average SOH error based on 10 years and the maximum value of the error based on 10 years of the estimated value of the actual life model of the battery can be calculated.

[0160] In addition, according to Fig.12 The experimental data in Fig.11 From the average data of the embodiment shown in , it is found that the error between the SOH value at 10 years and the estimated value of the actual life model of the battery is 0.85%p, the average error value over 10 years is 0.45%p, and the maximum value of the standard error over 10 years is 0.9%p.

[0161] As a result of the comparison, the maximum value of the 10-year standard error at each time point is within 1%p, and the error of the 10-year standard SOH (often used if determining the life of the battery) is also 0.85%p, and therefore, the applicability and reliability of the life estimation method according to one or more embodiments can be ensured.

[0162] The lifetime estimator 333 may estimate the lifetime of the battery module 10 by using a lifetime estimation model of the battery module 10 that best represents a scenario among results of a plurality of simulations of the reconstructed scenarios.

[0163] The foregoing is an illustration of some embodiments of the present disclosure and should not be construed as limiting the present disclosure. Although some embodiments have been described, it will be readily understood by those skilled in the art that various modifications are possible in the embodiments without departing from the spirit and scope of the present disclosure. It will be understood that the description of the features or aspects within each embodiment should generally be considered to be applicable to other similar features or aspects in other embodiments, unless otherwise described. Therefore, it will be apparent to those of ordinary skill in the art that the features, characteristics and / or elements described in conjunction with a particular embodiment may be used alone or in combination with the features, characteristics and / or elements described in conjunction with other embodiments, unless otherwise specifically indicated. Therefore, it should be understood that the foregoing is an illustration of various exemplary embodiments and should not be construed as being limited to the specific embodiments disclosed herein, and that various modifications to the disclosed embodiments and other exemplary embodiments are intended to be included in the spirit and scope of the present disclosure as defined in the claims and their equivalents.

Claims

1. A battery system comprising: Battery; a detection device configured to detect voltage, current and temperature from the battery; as well as The battery management system is configured as follows: estimating a state of charge based on the voltage, the current, and the temperature detected from the detection device; storing a profile generated by measuring a physical state of the battery in an event count format based on battery state data including the voltage, the current, the temperature, and the state of charge; generating a virtual scenario for driving a representative battery based on the configuration file; as well as The life span of the battery is estimated through the virtual scene.

2. The battery system according to claim 1, wherein: The battery management system comprises: A scenario generator is configured to generate a scenario of M days, where M is a natural number greater than or equal to 1; a scene corrector configured to generate N scenes of the M days based on the scenes of the M days, where N is a natural number greater than or equal to 2; and A lifetime estimator is configured to estimate the lifetime of the battery by simulating battery operation based on the N scenarios for the M days.

3. The battery system according to claim 2, wherein: The scenario generator is configured to generate the scenario template for the M days based on the occurrence probability of each of a plurality of stored representative templates; and Each of the plurality of stored representative templates includes a value representing a charge and discharge ratio of the battery in units of days as a probability.

4. The battery system according to claim 3, wherein: The scenario generator is configured to randomly sample the physical quantity at each time point when the charging state of the representative battery is switched in the scenario templates of the M days from the configuration file to generate a specific scenario for the M days.

5. The battery system according to claim 4, wherein: The configuration file includes: a charging log table generated based on data obtained from the battery status data when the battery is in a charging state; a discharge log table generated based on data obtained from among the battery state data when the battery is in a discharge state; and A rest log table is generated based on data obtained from among the battery state data when the battery is in a rest state.

6. The battery system according to claim 5, wherein: The scenario generator is configured to: deriving the physical quantity at the start time point of the charging period of the scenario template of the M days from the charging log table; Deriving the physical quantity at the start time point of the discharge period of the scenario template of the M days from the discharge log table; as well as The physical quantity at the start time point of the rest period of the scenario template of the M days is derived from the rest log table.

7. The battery system according to claim 2, wherein: The scene corrector is configured to restore N number of scenes among the scenes of the M days by using a Monte Carlo simulation method to generate the N scenes of the M days.

8. The battery system according to claim 2, wherein: The lifetime estimator is configured to: Simulating the N scenarios of the M days multiple times; Determining one simulation result among the simulation results of the multiple simulations as the life estimation model of the battery; as well as The lifetime of the battery is estimated using the lifetime estimation model.

9. A battery life estimation method, comprising: Detect voltage, current and temperature from the battery; estimating a state of charge based on the voltage, the current, and the temperature detected from the battery; generating a configuration file by measuring a physical state of the battery in an event count format based on battery state data including the voltage, the current, the temperature, and the state of charge, and storing the configuration file; generating a virtual scenario for driving a representative battery based on the configuration file; as well as The life span of the battery is estimated through the virtual scene.

10. The battery life estimation method according to claim 9, wherein: The estimation of the life of the battery includes: Generate a scenario of M days, where M is a natural number greater than or equal to 1; generating N scenes of the M days based on the scenes of the M days, where N is a natural number greater than or equal to 2; and The lifespan of the battery is estimated by simulating battery operation based on the N scenarios over the M days.

11. The battery life estimation method according to claim 10, wherein: The generating of the scenario of the M days comprises: generating a scenario template of the M days based on the occurrence probability of each of a plurality of stored representative templates, and Each of the plurality of stored representative templates includes a value representing a charge and discharge ratio of the battery in units of days as a probability.

12. The battery life estimation method according to claim 10, wherein: The generation of the scenario of the M days includes: based on each time point of switching the charging state of the representative battery in the scenario template of the M days, randomly sampling the physical quantity at each time point from the configuration file to generate a specific scenario of the M days.

13. The battery life estimation method according to claim 12, wherein: The generating of the configuration file includes: generating a charging log table based on data obtained from among the battery status data when the battery is in a charging state; generating a discharge log table based on data obtained from among the battery state data when the battery is in a discharged state; and A rest log table is generated based on data obtained from among the battery state data when the battery is in a rest state.

14. The battery life estimation method according to claim 13, wherein: The generation of the specific scene of the M days includes: deriving the physical quantity at the start time point of the charging period of the scenario template of the M days from the charging log table; deriving the physical quantity at the start time point of the discharge period of the scenario template of the M days from the discharge log table; and The physical quantity at the start time point of the rest period of the scenario template of the M days is derived from the rest log table.

15. The battery life estimation method according to claim 10, wherein: The generating of the N scenarios of the M days includes: restoring N number of scenarios among the scenarios of the M days by using a Monte Carlo simulation method to generate the N scenarios of the M days.

16. The battery life estimation method according to claim 10, wherein: Estimating the life of the battery by simulating the battery operation includes: Simulating the N scenarios of the M days multiple times; Determining one simulation result among the simulation results of the plurality of simulations as a life estimation model of the battery; and The lifetime of the battery is estimated using the lifetime estimation model.

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