Battery state estimation method and battery system providing the same

Abstract

The present invention relates to a battery state estimation method and a battery system that provides the method. The battery system of the present invention includes a battery including a plurality of battery cells, and a BMS (Battery Management System) that calculates a first SOC change amount, which is a difference value between a first SOC calculated at a predetermined first point in time before current begins between the battery and an external device and a second SOC calculated at a second point in time when the current ends, and a second SOC change amount, which is a difference value between the first SOC and a third SOC calculated at a third point in time a predetermined time has elapsed since the second point in time, and the BMS estimates the State of Health (SOH) of the battery based on the first SOC change amount and the second SOC change amount.

Description

[Technical Field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims the benefit of priority based on Korean Patent Application No. 10-2023-0051448 dated April 19, 2023, and all contents disclosed in the documents of this Korean patent application are incorporated herein by reference.

[0002] The present invention relates to a method for estimating the SOH (State of Health) of a battery and a battery system that provides the method. [Background technology]

[0003] Batteries installed in high-power products such as electric or hybrid vehicles contain multiple cells connected in series or parallel to supply high voltage to the load. For eco-friendly vehicles, battery performance is directly linked to the performance of the vehicle, so the role of a Battery Management System (BMS) that efficiently manages the battery status is important.

[0004] The BMS estimates the state of charge (SOC), state of health (SOH), and state of power (SOP) of the battery (or battery cell) based on the battery current flowing through the battery, the cell voltages of the battery cells, and the battery temperature (hereinafter referred to as battery data), and diagnoses the battery condition based on the estimated results.If an error is detected as a result of the diagnosis, the BMS transmits the diagnosis result to the upper system (e.g., automobile, motorcycle, ESS, etc.) in which the battery system is installed so that the overall safety and performance of the upper system can be managed.

[0005] However, due to the nonlinearity of battery cells, it is impossible to directly measure the battery's state of health (SOH). Therefore, the BMS includes multiple SOH estimation models, each of which estimates the battery's state of health (SOH) based on battery data.

[0006] Battery SOH is based on indirect estimation rather than direct measurement, which makes it unreliable. To overcome this issue, recent research and development has been conducted to estimate battery SOH by applying weighting values ​​to multiple SOH estimation models.

[0007] However, if the weighting ratio and the combination of estimation models are not appropriate, the result may be less reliable than a battery SOH estimated based on a single estimation model. Research and development is needed to improve the reliability of methods for estimating battery SOH by applying weighting values ​​to multiple SOH estimation models. Summary of the Invention [Problem to be solved by the invention]

[0008] An object of the present invention is to provide a battery state estimation method for estimating a battery's state of health (SOH) with high reliability and a battery system that provides the method. [Means for solving the problem]

[0009] A battery system according to one aspect of the present invention includes a battery including a plurality of battery cells, and a BMS (Battery Management System) that calculates a first SOC change amount, which is a difference value between a first State of Charge (SOC) calculated at a predetermined first point in time before current begins to flow between the battery and an external device and a second SOC calculated at a second point in time when the current ends, and a second SOC change amount, which is a difference value between the first SOC and a third SOC calculated at a third point in time a predetermined time has elapsed since the second point in time, and the BMS estimates a State of Health (SOH) of the battery based on the first SOC change amount and the second SOC change amount.

[0010] The BMS can calculate the first state of charge change amount based on a predetermined first algorithm that estimates the state of charge of the battery, and can calculate the second state of charge change amount based on the first algorithm and a predetermined second algorithm that estimates the state of charge of the battery.

[0011] The BMS calculates a first value by applying a first weight to a third state of charge change amount corresponding to a difference between the first state of charge and the third state of charge calculated based on the first algorithm, and calculates a second value by applying a second weight to a fourth state of charge change amount corresponding to a difference between the fourth state of charge calculated at the first time point and the fifth state of charge calculated at the third time point based on the second algorithm.

[0012] The BMS is a battery system that estimates the state of health of the battery based on the following formula:

number

[0013] SOC1_a is the first state of charge, SOC1_b is the second state of charge, SOC1_c is the third state of charge, SOC2_a is the fourth state of charge, SOC2_c is the fifth state of charge, α is the first weighting value, and β is the second weighting value.

[0014] The first algorithm can estimate the state of charge of the battery based on a value obtained by integrating the current flowing through the battery during the energization.

[0015] The second algorithm can estimate the state of charge of the battery based on a relationship between the state of charge and the open circuit voltage.

[0016] The first weight and the second weight may be determined according to a host system in which the battery is installed and a voltage value across the battery.

[0017] According to another aspect of the present invention, a battery state estimation method includes the steps of: calculating a first State of Charge (SOC) and a fourth State of Charge (SOC) based on a predetermined first algorithm and a predetermined second algorithm for estimating the state of charge of the battery at a first predetermined time point before the start of current flow between the battery and an external device; calculating a second State of Charge (SOC) based on the first algorithm at a second time point when the current flow ends; calculating a third State of Charge (SOC) and a fifth State of Charge (SOC) based on the first algorithm and the second algorithm at a third time point when a predetermined time has elapsed from the second time point; and estimating a State of Health (SOH) of the battery based on a first State of Charge change amount, which is a difference value between the first State of Charge and the second State of Charge; a third State of Charge change amount, which is a difference value between the first State of Charge and the third State of Charge; and a fourth State of Charge change amount, which is a difference value between the fourth State of Charge and the fifth State of Charge.

[0018] The step of estimating the state of health of the battery may include calculating a first value by applying a first weighting value to a third state of charge change amount, calculating a second value by applying a second weighting value to a fourth state of charge change amount, calculating the second state of charge change amount by combining the first value and the second value, and estimating the state of health based on the first state of charge change amount and the second state of charge change amount.

[0019] The battery state estimation method, wherein the step of estimating the battery state of health estimates the state of health based on the following formula:

number

[0020] SOC1_a is the first state of charge, SOC1_b is the second state of charge, SOC1_c is the third state of charge, SOC2_a is the fourth state of charge, SOC2_c is the fifth state of charge, α is the first weighting value, and β is the second weighting value.

[0021] The first algorithm can estimate the state of charge of the battery based on a value obtained by integrating the current flowing through the battery during the energization.

[0022] The second algorithm can estimate the state of charge of the battery based on a relationship of state of charge corresponding to open circuit voltage. [Effects of the Invention]

[0023] The present invention can apply multiple estimation models and multiple weighting factors to estimate the battery state of health (SOH) with high reliability.

[0024] The present invention can improve the reliability of the estimated battery state of health (SOH) by calculating the estimated time point of the battery state of charge (SOC), which is the basis for calculating the battery state of health (SOH), based on a state of charge (SOC) estimation model and battery characteristics.

[0025] The present invention estimates the battery state of health (SOH) by reflecting weights optimized for the environment in which the battery is used, i.e., the host system in which the battery system is installed, in multiple estimation models, thereby increasing the reliability of the estimated results.

[0026] The present invention estimates the battery state of health (SOH) by reflecting weighted values ​​that take into account not only the host system in which the battery system is installed but also the current state of the battery (such as cell voltage) in multiple estimation models, thereby increasing the reliability of the estimated results. [Brief explanation of the drawings]

[0027] [Figure 1] 1 is a conceptual diagram illustrating a host system in which a battery system according to an embodiment is mounted.

[0028] [Figure 2] FIG. 2 is a block diagram illustrating the battery system of FIG. 1 in more detail.

[0029] [Figure 3] 3 is a block diagram illustrating in detail the information stored in the storage unit of FIG. 2. FIG.

[0030] [Figure 4] 4 is a diagram illustrating changes in the state of charge (SOC) of a battery in a discharge mode according to an embodiment.

[0031] [Figure 5] 10 is a flowchart illustrating a battery state estimation method according to another embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0032] Hereinafter, the embodiments disclosed herein will be described in detail with reference to the accompanying drawings. Identical or similar elements will be designated by identical or similar reference numerals, and redundant descriptions thereof will be omitted. The suffixes "model" and / or "part" for elements used in the following description are added or used interchangeably solely for ease of description and do not have any distinct meanings or functions. Furthermore, when describing the embodiments disclosed herein, if a detailed description of related prior art is deemed to detract from the gist of the embodiments disclosed herein, such a detailed description will be omitted. Furthermore, the accompanying drawings are intended to facilitate understanding of the embodiments disclosed herein, and the accompanying drawings should not be construed as limiting the technical concepts disclosed herein, but should be understood to include all modifications, equivalents, or alternatives within the concept and technical scope of the present invention.

[0033] Terms including ordinal numbers such as first, second, etc. may be used to describe various components, but the components are not limited by the terms. These terms are used only to distinguish one component from another.

[0034] When a component is referred to as being "coupled" or "connected" to another component, it should be understood that the component may be directly coupled or connected to the other component, but that there may be other components in between. On the other hand, when a component is referred to as being "directly coupled" or "directly connected" to another component, it should be understood that there are no other components in between.

[0035] In this application, terms such as "comprise" or "have" are to be understood as specifying the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but not as precluding the possible presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0036] FIG. 1 is a conceptual diagram illustrating a host system in which a battery system according to one embodiment is installed, FIG. 2 is a block diagram illustrating the battery system of FIG. 1 in detail, and FIG. 3 is a block diagram illustrating information stored in the storage unit of FIG. 2 in detail.

[0037] Referring to FIG. 1, a host system 1 is a system in which a battery system 2 is mounted.

[0038] The upper system 1 may include any system equipped with a battery, for example, an automobile, a motorcycle, an energy storage system (ESS), etc.

[0039] The battery system 2 includes a custom SOX (State of X) estimation algorithm in the host system 1. According to one embodiment, the battery system 2 identifies the host system 1 in which the current battery system 2 is installed and estimates the battery's State of Health (SOH) using the corresponding SOH estimation algorithm.

[0040] As a result, even when a standard battery that can be used in various host systems 1 is installed and used in a specific host system 1, the battery system 2 can estimate the battery's State of Health (SOH) that accurately reflects the characteristics of the individual host system 1.

[0041] Referring to FIG. 2, the battery system 2 includes a battery 10, a relay 20, a current sensor 30, and a BMS (Battery Management System) 40.

[0042] The battery 10 may include a plurality of battery cells electrically connected in series and in parallel. In some embodiments, the battery cells may be rechargeable secondary batteries. A predetermined number of battery cells may be connected in series to form a battery module, a predetermined number of battery modules may be connected in series to form a battery pack, or a predetermined number of battery packs may be connected in parallel to form a battery bank, thereby supplying a desired amount of power. While FIG. 1 illustrates the battery 10 having a plurality of battery cells connected in series, the battery 10 is not limited thereto, and may be configured as a battery module, a battery pack, or a battery bank.

[0043] Each of the plurality of battery cells is electrically connected via wiring to the BMS 40. The BMS 40 exchanges and analyzes various information related to the plurality of battery cells, controls charging and discharging of the battery cells, protection operations, etc., and can control the operation of the relay 20.

[0044] 2, a battery 10 includes a plurality of battery cells connected in series and is connected between two output terminals (OUT1, OUT2) of a battery system 2, a relay 20 is connected between the positive terminal of the battery system 2 and the first output terminal (OUT1), and a current sensor 30 is connected between the negative terminal of the battery system 2 and the second output terminal (OUT2). The configuration and the connection relationship between the configurations shown in FIG. 1 are merely examples, and the invention is not limited thereto.

[0045] The relay 20 controls the electrical connection between the battery system 2 and the external device. When the relay 20 is turned on, the battery system 2 and the external device are electrically connected to each other to perform charging or discharging, and when the relay 20 is turned off, the battery system 2 and the external device are electrically disconnected. In this case, the external device may be a charger in a charging cycle that supplies power to the battery 10 to charge it, or a load in a discharging cycle that the battery 10 discharges power to the external device.

[0046] The current sensor 30 is connected in series to a current path between the battery 10 and an external device. The current sensor 30 can measure the battery current, i.e., the charging current and discharging current, flowing through the battery 10 and transmit the measurement result to the BMS 40. For example, when multiple battery cells are connected in series, the battery current can correspond to the cell current.

[0047] The BMS 40 includes a monitoring unit 41 , a storage unit 43 , a communication unit 45 , and a control unit 47 .

[0048] The monitoring unit 41 is electrically connected to the positive and negative electrodes of each of the plurality of battery cells and measures the cell voltage of each of the plurality of battery cells. The battery current value measured by the current sensor 30 and the battery temperature value measured by a temperature sensor (not shown) can be transmitted to the monitoring unit 41. The monitoring unit 41 transmits information on the measured cell voltage, battery current, battery current, and battery temperature to the control unit 47.

[0049] For example, the monitoring unit 41 may measure the cell voltage of each of the plurality of battery cells at predetermined intervals during a rest period when no charging or discharging occurs, and may calculate the cell current based on the measured cell voltage. The monitoring unit 41 may transmit the cell voltage and cell current of each of the plurality of battery cells to the control unit 47.

[0050] The storage unit 43 stores system identification information (APP ID), weights, a plurality of estimation models for estimating the battery state (SOX, State of X), and battery information.

[0051] The battery state (SOX, State of X) may include the state of health (SOH, State of Health) of the battery 10. In this case, the battery information may include information related to the battery, such as cell voltage, cell current, battery current, and battery temperature.

[0052] 3, the storage unit 43 may store a plurality of SOC estimation models (SOC1, SOC2) for estimating the state of charge (SOC) of the battery 10 based on battery information using a predetermined algorithm, at least one SOH estimation model (SOH) for estimating the state of health (SOH) of the battery 10, and a lookup table for weighting values. While FIG. 3 illustrates two SOC estimation models, i.e., a first SOC estimation model and a second SOC estimation model, the present invention is not limited thereto, and the BMS 40 may include three or more SOC estimation models. Furthermore, while FIG. 3 illustrates one SOH estimation model, the present invention is not limited thereto, and the BMS 40 may include two or more SOH estimation models.

[0053] For example, the storage unit 43 may store a first SOC estimation model that estimates the state of charge (SOC) using the widely known Coulomb Counting Method, a second SOC estimation model that estimates the state of charge (SOC) based on the OCV (Open Circuit Voltage Method)-SOC relationship, a third SOC estimation model that estimates the state of charge (SOC) based on the terminal voltage, and the like.

[0054] For example, the storage unit 43 may store a first SOH estimation model that estimates the state of health (SOH) based on the widely known OCV-SOH relationship, a second SOH estimation model that estimates the state of health (SOH) based on the SOC-SOH relationship, a third SOH estimation model that estimates the state of health (SOH) based on the direct current internal resistance (DCIR) of the battery cell, and the like.

[0055] The system identification information (APP ID) may be identification information that distinguishes a system in which the battery system 2 is installed. For example, the storage unit 43 may store various system identification information (APP ID) such as identification information for a vehicle system (E_Vehicle, APP ID=001), identification information for a bike system (E_Bike, APP ID=002), and identification information for an ESS system (E_ESS, APP ID=003).

[0056] The weighted value may be a value that is preset in the upper system 1 to estimate the state of order (SOX) of the battery and stored in the storage unit 43. According to one embodiment, the weighted value may include a plurality of first weighted values ​​corresponding to the system identification information (APP ID). According to another embodiment, the weighted value may include a plurality of second weighted values ​​corresponding to the system identification information (APP ID) and predetermined estimation conditions. A more detailed description will be given below together with the control unit 47.

[0057] The estimation condition may be a condition that reflects the current state of the battery cell. For example, the estimation condition may be determined based on a result of comparing the cell voltage of the battery cell with a predetermined reference value. As another example, the estimation condition may include a condition determined by the cell voltage, battery current, or battery temperature of the battery cell. However, the estimation condition is not limited to the cell voltage, battery current, and battery temperature, and may include various conditions that reflect the current state of the battery 10 or the battery cell.

[0058] The communication unit 45 communicates with the higher-level system 1 to receive identification information (hereinafter, system identification information) of the higher-level system 1. For example, the control unit 47 can store the system identification information (APP ID) received through the communication unit 45 in the storage unit 43.

[0059] The control unit 47 determines a weighting value corresponding to at least one of the system identification information (APP ID) and the predetermined estimation conditions based on a lookup table. The control unit 47 applies the determined weighting value to a plurality of estimation results estimated by a plurality of SOX estimation models, and calculates an average value by summing up the plurality of estimation results to which the weighting value has been applied.

[0060] According to an embodiment, the control unit 47 may calculate the state of health (SOH) of the battery 10 based on the average value of the weighted SOC values. A specific method for calculating the state of health (SOH) will be described below with reference to FIG. 4.

[0061] The following Table 1 shows an example of a plurality of first weights corresponding to predetermined system identification information (APP ID). As described above, the first weight may be a weight that takes into consideration only the system identification information (APP ID), and the corresponding value may differ for each estimation model, as shown in Table 1.

[0062] [Table 1]

[0063] For example, referring to Table 1, it is assumed that the upper system 1 is a vehicle system (E_Vehicle, APP ID=001). The control unit 47 may calculate an average value of the state of charge (SOC) of the battery 10 by applying a first weighting value of 0.7 corresponding to the first SOC value estimated through the first SOC estimation model and a first weighting value of 0.3 corresponding to the second SOC value estimated through the second SOC estimation model.

[0064] Specifically, if the first SOC value estimated by the first SOC estimation model is 50% and the second SOC value estimated by the second SOC estimation model is 54%, the control unit 47 can calculate the average value of the state of charge (SOC) to be 51.2% (50% x 0.7 + 54% x 0.3 = 51.2%).

[0065] The following Table 2 shows an example of a plurality of second weights corresponding to a predetermined system identification information (APP ID) and estimation conditions. As described above, the second weight may be a weight that takes into consideration all of the system identification information (APP ID) and estimation conditions, and the corresponding value may differ for each estimation model, as shown in the following Table 2.

[0066] [Table 2]

[0067] In Table 2, the estimation conditions are described as conditions determined by the comparison result between the cell voltage and a predetermined reference value (e.g., 3.7V), but as described above, the estimation conditions are not limited to the cell voltage and the reference value and can be applied to 10 batteries in the same manner. In this case, the cell voltage may indicate the average value of the cell voltages of the plurality of battery cells constituting the battery module, but is not limited thereto and may indicate the median or average value of the plurality of cell voltages.

[0068] For example, referring to Table 2, it is assumed that the upper system 1 is a vehicle system (E_Vehicle, APP ID=001) and the cell voltage of the battery cells included in the current battery 10 is 3.7 V or higher. Referring to Table 2, the control unit 47 may apply a second weighting value of 0.7 corresponding to the first SOC value estimated through the first SOC estimation model and a second weighting value of 0.3 corresponding to the second SOC value estimated through the second SOC estimation model to calculate an average value of the state of charge (SOC) for the battery cells or the battery 10.

[0069] Specifically, if the first SOC value estimated through the first SOC estimation model is 50% and the second SOC value estimated through the second SOC estimation model is 54%, the control unit 47 can calculate the average value of the state of charge (SOC) for the battery cell or battery 10 to be 51.2% (50% x 0.7 + 54% x 0.3 = 51.2%).

[0070] As another example, referring to Table 2, it is assumed that the upper system 1 is a motorcycle system (E_Bike, APP ID=002) and the cell voltage of the battery cells included in the current battery 10 is less than 3.7 V. Referring to Table 2, the control unit 47 may apply a second weighting value of 0.5 corresponding to the first SOC value estimated through the first SOC estimation model and a second weighting value of 0.5 corresponding to the second SOC value estimated through the second SOC estimation model to calculate an average value of the state of charge (SOC) for the battery cells or the battery 10.

[0071] Specifically, if the first SOC value estimated through the first SOC estimation model is 50% and the second SOC value estimated through the second SOC estimation model is 54%, the control unit 47 can calculate the average value of the state of charge (SOC) for the battery cell or battery 10 to be 52% (50% x 0.5 + 54% x 0.5 = 52%).

[0072] Referring to FIG. 3, Tables 1 and 2 may be examples of weight lookup tables. However, the weight lookup table is not limited thereto and may be created in various formats. Tables 1 and 2 only describe the state of charge (SOC) of the battery 10, but the present invention is not limited thereto. According to an embodiment, the control unit 47 may estimate the state of health (SOH) of the battery 10 based on the average value of the state of charge (SOC) of the battery 10 calculated by the above-described method. Hereinafter, a method in which the control unit 47 estimates the state of health (SOH) of the battery 10 will be described in detail with reference to FIG. 4.

[0073] FIG. 4 is a diagram illustrating a change in the state of charge (SOC) of a battery in a discharging mode according to an embodiment.

[0074] 4 is a graph showing the change in the state of charge (SOC) of the battery 10 over time in a discharge mode in which the battery 10 supplies power to an external device. Graph 1 (A) may be a graph showing the change in the state of charge (SOC) of the battery 10 estimated using only one algorithm. Graph 2 (B) may be a graph showing the change in the state of charge (SOC) of the battery 10 estimated using multiple algorithms.

[0075] For example, the second graph (B) may be a graph that more accurately reflects the current state of the battery 10 than the first graph (A). In Figure 4, the first graph (A) is shown by a solid line, and the second graph (B) is shown by a dotted line. Hereinafter, a method for calculating the state of health (SOH) of the battery 10 based on the first graph (A) will be described by way of an embodiment.

[0076] The first time point (Ta) may be the time when current flow of the battery 10 begins or a predetermined time point before the current flow begins. The second time point (Tb) may be the time when current flow of the battery 10 ends. The third time point (Tc) may be the time when a predetermined waiting time (T_th) has elapsed since the second time point (Tb).

[0077] The waiting time (T_th) can be determined experimentally based on various factors, such as the algorithm for estimating the SOC and / or the characteristics of the battery 10. For example, in order to accurately measure the open circuit voltage (OCV), it is necessary to measure the OCV after a predetermined time has elapsed since the end of current application to the battery 10, so that the state of the battery 10 has stabilized. Referring to FIG. 4, when the SOC estimation model includes an algorithm for estimating the SOC based on the OCV, the SOC can be estimated based on the OCV measured at a third time point (Tc) after a predetermined time has elapsed since the end of current application to the battery 10. This allows for a more accurate SOC estimation than when the SOC is estimated based on the OCV measured at a second time point (Tb) after the end of current application to the battery 10. According to an embodiment, the waiting time (T_th) can be set depending on the type of battery 10, taking into account the time it takes for the battery 10 to stabilize after the end of current application.

[0078] The control unit 47 can calculate the state of health (SOH) of the battery 10 based on the following formulas 1 to 3. For example, the control unit 47 can calculate the state of health (SOH) of the battery 10 based on an algorithm (SOH estimation model) corresponding to the following formulas 1 to 3.

number

[0079]

number

[0080]

number

[0081] Referring to Equation 1, the first state of charge change amount (ΔSOC_ab) may be the state of charge change amount of the battery 10 during a period from a first point in time (Ta) to a second point in time (Tb). The second state of charge change amount (ΔSOC_ac) may be the state of charge change amount of the battery 10 during a period from the first point in time (Ta) to a third point in time (Tc). Referring to Equation 1, the control unit 47 can calculate the state of health (SOH) of the battery 10 based on the ratio of the first state of charge change amount (ΔSOC_ab) to the second state of charge change amount (ΔSOC_ac).

[0082] Equation 2 may be a more detailed expression of Equation 1. Equation 3 may be a more detailed expression of Equation 2. Referring to Equation 2 and Equation 3, the state of charge (SOC1) may be a state of charge (SOC) calculated based on a first SOC estimation model. The state of charge (SOC2) may be a state of charge (SOC) calculated based on a second SOC estimation model.

[0083] According to an embodiment, the first SOC estimation model may be an algorithm that estimates the state of charge (SOC) using a current integration method (Coulomb Counting Method). The current integration method may be an algorithm that estimates the state of charge (OSC) of the battery 10 based on a value obtained by integrating the current flowing through the battery 10 while it is energized. For example, the current integration method may correspond to the following Equation 4:

[0084]

number

[0085] In Equation 4, SOC(t) is the state of charge (SOC) at a predetermined time (t), SOC(0) is the initial (t=0) state of charge, C is the rated capacity of the battery 10, and I is the battery current flowing through the battery 10 during the current flow process. However, the first SOC estimation model is not limited to Equation 4 and may include various existing algorithms that estimate the state of charge (SOC) of the battery 10 by accumulating the battery current flowing through the battery 10 during the charge or discharge process.

[0086] The second SOC estimation model may be an algorithm that estimates the SOC based on an "OCV-SOC relationship table" or "OCV-SOC relationship graph" that shows the relationship between the SOC and the open circuit voltage (OCV). For example, the OCV-SOC relationship graph is a graph that shows the SOC on the horizontal axis and the OCV on the vertical axis, and can be calculated experimentally. If a specific OCV value is known, the corresponding SOC can be easily determined.

[0087] 4, the control unit 47 calculates the first state of charge change amount (ΔSOC1_ab). Specifically, the control unit 47 calculates a first state of charge (SOC1_a) corresponding to a first time point (Ta) based on the first SOC estimation model and a second state of charge (SOC1_b) corresponding to a second time point (Tb) based on the first SOC estimation model. The control unit 47 calculates the first state of charge change amount (ΔSOC1_ab) corresponding to the difference between the first state of charge (SOC1_a) and the second state of charge (SOC1_b).

[0088] The first state of charge change amount (ΔSOC1_ab) may be a state of charge change amount of the battery 10 calculated during a period from a first time point (Ta) to a second time point (Tb) based on the first SOC estimation model. Also, referring to Equation 1 and Equation 2, the first state of charge change amount (ΔSOC_ab) may correspond to the first state of charge change amount (ΔSOC1_ab).

[0089] The control unit 47 calculates a third state of charge change amount (ΔSOC1_ac). Specifically, the control unit 47 calculates a third state of charge (SOC1_c) corresponding to a third time point (Tc) based on the first SOC estimation model. The control unit 47 calculates the third state of charge change amount (ΔSOC1_ac) corresponding to a difference between the first state of charge (SOC1_a) and the third state of charge (SOC1_c).

[0090] The control unit 47 calculates a fourth state of charge change amount (ΔSOC2_ac). Specifically, the control unit 47 calculates a fourth state of charge (SOC2_a) corresponding to the first time point (Ta) based on the second SOC estimation model. The control unit 47 calculates a fifth state of charge (SOC2_c) corresponding to the third time point (Tc) based on the second SOC estimation model. The control unit 47 calculates the fourth state of charge change amount (ΔSOC2_ac) corresponding to the difference between the fourth state of charge (SOC2_a) and the fifth state of charge (SOC2_c).

[0091] The control unit 47 applies a first weighting value (α) to the third state of charge change amount (ΔSOC1_ac) to calculate a first denominator value (α(ΔSOC1_ac)). The control unit 47 applies a second weighting value (β) to the fourth state of charge change amount (ΔSOC2_ac) to calculate a second denominator value (β(ΔSOC2_ac)). The control unit 47 combines the first denominator value (α(ΔSOC1_ac)) and the second denominator value (β(ΔSOC2_ac)) to calculate a fifth state of charge change amount (α(ΔSOC1_ac)+β(ΔSOC2_ac)).

[0092] The fifth state of charge change amount (α(ΔSOC1_ac)+β(ΔSOC2_ac)) may correspond to the state of charge change amount of the battery 10 calculated during the period from the first time point (Ta) to the third time point (Tc) based on the first SOC estimation model and the second SOC estimation model. Also, referring to Equation 1 and Equation 3, the second state of charge change amount (ΔSOC_ac) may correspond to the fifth state of charge change amount (α(ΔSOC1_ac)+β(ΔSOC2_ac)).

[0093] The control unit 47 can calculate the state of health (SOH) of the battery 10 based on the ratio of the first state of charge change amount (ΔSOC1_ab) to the fifth state of charge change amount (α(ΔSOC1_ac)+β(ΔSOC2_ac)).

[0094] FIG. 5 is a flowchart illustrating a battery state estimation method according to another embodiment.

[0095] Referring to Figures 4 and 5, at a predetermined first time point (Ta) before the start of current flow between the battery 100 and the external device, the BMS 40 calculates a first state of charge (SOC1_a) and a fourth state of charge (SOC2_a) based on a predetermined first algorithm and a predetermined second algorithm, respectively, for estimating the state of charge of the battery 10 (S100).

[0096] According to an embodiment, the first algorithm may be an algorithm corresponding to a Coulomb Counting Method that estimates the state of charge of the battery 10 based on an integrated value of the current flowing through the battery 10 during power-on. For example, the Coulomb Counting Method may correspond to Equation 4.

[0097] According to an embodiment, the first algorithm may correspond to an algorithm for estimating the state of charge (SOC) of the battery 10 based on an OCV-SOC relationship table or an OCV-SOC relationship graph showing the relationship between the open circuit voltage (OCV) and the corresponding state of charge (SOC).

[0098] Next, at a second time point (Tb) when the energization ends, the BMS 40 calculates a second state of charge (SOC1_b) based on the first algorithm (S200).

[0099] Next, at a third time point (Tc) after a predetermined time has elapsed since the second time point (Tb), the BMS40 calculates a third state of charge (SOC1_c) and a fifth state of charge (SOC2_c) based on the first algorithm and the second algorithm, respectively (S300).

[0100] The third point in time (Tc) may be a point in time when a predetermined waiting time (T_th) has elapsed since the second point in time (Tb). The waiting time (T_th) may be determined experimentally based on various factors, such as the algorithm for estimating the state of charge (SOC) and / or the characteristics of the battery 10. For example, in order to accurately measure the open circuit voltage (OCV), it is necessary to measure the OCV after a predetermined time has elapsed since the end of power supply to the battery 10 and the state of the battery 10 has stabilized. Referring to FIG. 4, when the SOC estimation model includes an algorithm for estimating the state of charge (SOC) based on the OCV, the SOC can be estimated based on the open circuit voltage (OCV) measured at the third point in time (Tc), a predetermined time after the end of power supply to the battery 10. This allows for a more accurate SOC estimation than estimating the SOC based on the open circuit voltage (OCV) measured at the second point in time (Tb), when power supply to the battery 10 is terminated. According to the embodiment, the standby time (T_th) can be set depending on the type of the battery 10, taking into consideration the time required for stabilization after the end of power supply.

[0101] Next, the BMS 40 estimates the state of health (SOH) of the battery based on the first to fifth states of charge (SOC1_a, SOC1_b, SOC1_c, SOC2_a, SOC2_c) (S400).

[0102] The BMS 40 calculates a first state of charge variation (ΔSOC_ab) which is a difference between the first state of charge (SOC1_a) and the second state of charge (SOC1_b). Specifically, referring to Equation 1, the first state of charge variation (ΔSOC_ab) may be a variation in the state of charge of the battery 10 estimated based on the first algorithm during a period from a first point in time (Ta) to a second point in time (Tb).

[0103] The BMS 40 calculates a third state of charge change (ΔSOC1_ac) which is a difference between the first state of charge (SOC1_a) and the third state of charge (SOC1_c). Specifically, referring to Equation 2, the third state of charge change (ΔSOC1_ac) may be a change in the state of charge of the battery 10 estimated based on the first algorithm during the period from the first time point (Ta) to the third time point (Tc).

[0104] The BMS 40 calculates a fourth state of charge change (ΔSOC2_ac) which is a difference between the fourth state of charge (SOC2_a) and the fifth state of charge (SOC2_c). Specifically, referring to Equation 2, the fourth state of charge change (ΔSOC2_ac) may be a change in the state of charge of the battery 10 estimated based on the second algorithm during the period from the first time point (Ta) to the third time point (Tc).

[0105] The BMS 40 calculates a second state of charge change amount (ΔSOC_ac) based on the third state of charge change amount (ΔSOC1_ac) and the fourth state of charge change amount (ΔSOC2_ac). Referring to FIG. 4, Equation 2, and Equation 3, the second state of charge change amount (ΔSOC_ac) may be the state of charge change amount of the battery 10 estimated based on the first algorithm and the second algorithm during the period from the first time point (Ta) to the third time point (Tc).

[0106] Specifically, the BMS 40 applies a first weighting value (α) to the third state of charge change amount (ΔSOC1_ac) to calculate a first value (α(ΔSOC1_ac)), and applies a second weighting value (β) to the fourth state of charge change amount (ΔSOC2_ac) to calculate a second value (β(ΔSOC2_ac)). The BMS 40 combines the first and second values ​​to calculate a second state of charge change amount (ΔSOC_ac).

[0107] The BMS 40 estimates the state of health (SOH) of the battery 10 based on the first state of charge change amount (ΔSOC_ab) and the second state of charge change amount (ΔSOC_ac). Referring to Equation 1, the BMS 40 estimates the state of health (SOH) of the battery 10 based on the ratio of the first state of charge change amount (ΔSOC_ab) to the second state of charge change amount (ΔSOC_ac).

[0108] Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited to these examples, and various modifications and improvements made by those skilled in the art to which the present invention pertains also fall within the scope of the present invention.

Claims

1. Battery, and a battery management system (BMS) that calculates a first SOC change amount, which is a difference between a first SOC calculated at a predetermined first time point before energization between the battery and an external device begins and a second SOC calculated at a second time point when energization ends, and a second SOC change amount, which is a difference between the first SOC and a third SOC calculated at a third time point when a predetermined time has elapsed since the second time point; The BMS includes: The battery system estimates a state of health (SOH) of the battery based on the first amount of change in state of charge and the second amount of change in state of charge.

2. The BMS includes:

2. The battery system of claim 1, wherein the first amount of change in state of charge is calculated based on a predetermined first algorithm that estimates the state of charge of the battery, and the second amount of change in state of charge is calculated based on the first algorithm and a predetermined second algorithm that estimates the state of charge of the battery.

3. The BMS includes:

3. The battery system of claim 2, wherein a first value is calculated by applying a first weight to a third state of charge change amount corresponding to a difference between the first state of charge and the third state of charge calculated based on the first algorithm, a second value is calculated by applying a second weight to a fourth state of charge change amount corresponding to a difference between a fourth state of charge calculated at the first time point and a fifth state of charge calculated at the third time point based on the second algorithm, and the first value and the second value are combined to calculate the second state of charge change amount.

4. The BMS includes: The battery system according to claim 3 , wherein the state of health of the battery is estimated based on the following formula: [Equation 1] SOC1_a is the first state of charge, SOC1_b is the second state of charge, SOC1_c is the third state of charge, SOC2_a is the fourth state of charge, SOC2_c is the fifth state of charge, α is the first weight value, and β is the second weight value.

5. The first algorithm:

4. The battery system according to claim 3, wherein the state of charge of the battery is estimated based on an integrated value of the current flowing through the battery during the energization.

6. The second algorithm:

4. The battery system of claim 3, wherein the state of charge of the battery is estimated based on a relationship of the state of charge corresponding to the open circuit voltage.

7. The first weight and the second weight are The battery system according to claim 3 , wherein the voltage is determined based on a host system in which the battery is installed and a voltage value across the battery.

8. calculating a first state of charge (SOC) and a fourth state of charge (SOC) based on a first predetermined algorithm and a second predetermined algorithm for estimating a state of charge of the battery at a first predetermined time point before energization between the battery and the external device begins; calculating a second state of charge based on a first algorithm at a second time when the energization is terminated; calculating a third state of charge and a fifth state of charge based on the first algorithm and the second algorithm, respectively, at a third point in time that is a predetermined time after the second point in time; and a first state of charge change amount that is a difference value between a first state of charge and a second state of charge, a third state of charge change amount that is a difference value between the first state of charge and a third state of charge, and a fourth state of charge change amount that is a difference value between the fourth state of charge and a fifth state of charge.

9. The step of estimating the state of health of the battery includes:

9. The battery state estimation method according to claim 8, further comprising: calculating a first value by applying a first weight to a third amount of change in the state of charge; calculating a second value by applying a second weight to a fourth amount of change in the state of charge; calculating a second amount of change in the state of charge by combining the first value and the second value; and estimating the state of health based on the first amount of change in the state of charge and the second amount of change in the state of charge.

10. The step of estimating the state of health of the battery includes: The battery state estimation method according to claim 9 , wherein the state of health is estimated based on the following formula: [Equation 2] SOC1_a is the first state of charge, SOC1_b is the second state of charge, SOC1_c is the third state of charge, SOC2_a is the fourth state of charge, SOC2_c is the fifth state of charge, α is the first weight value, and β is the second weight value.

11. The first algorithm:

9. The method for estimating a battery state according to claim 8, wherein the state of charge of the battery is estimated based on an integrated value of the current flowing through the battery during the energization.

12. The second algorithm:

9. The method of claim 8, further comprising estimating the state of charge of the battery based on a relationship of the state of charge corresponding to the open circuit voltage.

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