Battery state prediction method and battery system providing same

Abstract

The present invention discloses a battery state prediction method and a battery system providing the same, the system comprising: a battery including a plurality of battery cells; and a battery management system (BMS) that calculates a first state of charge change amount as a difference between a first state of charge (SOC) and a second state of charge, the first state of charge being calculated at a predetermined first point in time before the start of current flow between the battery and the external device, the first charging state is calculated at a first point in time when the current flow ends, and the second charging state is calculated at a second point in time when the current flow ends, and a second charging state change amount is calculated as a difference between the first charging state and a third charging state calculated at a third point in time after a predetermined time elapses from the second point in time, the BMS predicts a state of health (SOH) of the battery based on the first state of charge change amount and the second state of charge change amount.

Description

Technical Field

[0001] Cross - Reference to Related Applications

[0002] This application claims the priority and benefit of Korean Patent Application No. 10 - 2023 - 0051448, filed with the Korean Intellectual Property Office on April 19, 2023, the entire contents of which are incorporated herein by reference.

[0003] The present disclosure relates to a method for predicting a state of health (SOH) of a battery and a battery system providing the method. Background Art

[0004] Batteries installed in high - output products such as electric vehicles or hybrid vehicles may need to supply high voltage to a load, and thus include a plurality of cells connected in series or parallel to each other. In an eco - friendly vehicle, the performance of the battery is directly related to the performance of the vehicle, and thus the role of a battery management system (BMS) that effectively manages the battery state may be important.

[0005] The BMS can predict a state of charge (SOC), a state of health (SOH), and a state of power (SOP) of a battery (or battery cells) based on battery current flowing through the battery, a plurality of cell voltages of a plurality of battery cells, battery temperature, etc. (hereinafter referred to as battery data), and diagnose the battery state based on the prediction results. When an error occurs in the diagnosis result, the BMS can transmit the diagnosis result to an upper - layer system (e.g., an automobile, a bicycle, or an energy storage system (ESS)) in which the battery system is installed, thereby managing the overall safety and performance of the upper - layer system.

[0006] Meanwhile, due to the non - linearity of battery cells, the state of health (SOH) of a battery cannot be directly measured. Thus, the BMS may include a plurality of SOH prediction models, and each of the plurality of prediction models can predict the state of health (SOH) of the battery based on battery data.

[0007] The state of health (SOH) of a battery may not be highly reliable because the SOH of the battery is based on results obtained from an indirect prediction method rather than a direct measurement method. To solve this problem, research and development are recently being conducted to predict the state of health (SOH) of a battery by applying weights to a plurality of SOH prediction models.

[0008] However, when the weight application ratio and the combination of prediction models are inappropriate, results that are less reliable than the state of health (SOH) of a battery predicted based on a single prediction model may be obtained. Research and development are needed to increase the reliability of a method for predicting the state of health (SOH) of a battery by applying weights to a plurality of SOH prediction models. Summary of the Invention

[0009]

Technical Problem

[0010] The present disclosure attempts to provide a highly reliable battery state prediction method for predicting the state of health (SOH) of a battery and a battery system providing the method.

[0011]

Technical solution

[0012] According to one embodiment, a battery system includes: a battery including a plurality of battery cells; and a battery management system (BMS) that calculates a first state of charge change amount, the first state of charge change amount being a difference between a first state of charge (SOC) and a second state of charge, the first state of charge being calculated at a predetermined first time point before the start of current flow between the battery and an external device, and the second state of charge being calculated at a second time point at the end of the current flow, and calculates a second state of charge change amount, the second state of charge change amount being a difference between the first state of charge and a third state of charge, the third state of charge being calculated at a third time point after a predetermined time has elapsed from the second time point, wherein the BMS predicts the state of health (SOH) of the battery based on the first state of charge change amount and the second state of charge change amount.

[0013] The BMS may calculate the first state of charge change amount based on a predetermined first algorithm for predicting the state of charge of the battery, and calculate the second state of charge change amount based on the first algorithm and a predetermined second algorithm for predicting the state of charge of the battery.

[0014] The BMS may calculate 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, calculate a second value 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 calculate the second state of charge change amount by adding the first value and the second value to each other.

[0015] The BMS predicts the state of health of the battery based on the following equation:

[0016]

[0017] (Here, SOC1_a indicates the first state of charge, SOC1_b indicates the second state of charge, SOC1_c indicates the third state of charge, SOC2_a indicates the fourth state of charge, SOC2_c indicates the fifth state of charge, α indicates the first weight, and β indicates the second weight).

[0018] The first algorithm may predict the state of charge of the battery based on an integrated value of the current flowing through the battery during current flow.

[0019] The second algorithm can predict the state of charge of the battery based on the relationship between the open-circuit voltage and the state of charge corresponding to the open-circuit voltage.

[0020] The first weight and the second weight can be determined based on the upper-level system in which the battery is installed and the voltage value across the battery.

[0021] According to another embodiment, a method for predicting the state of a battery includes: at a predetermined first time point before the start of current flow between the battery and an external device, calculating a first state of charge (SOC) and a fourth state of charge respectively based on a predetermined first algorithm and a predetermined second algorithm for predicting the state of charge of the battery; at a second time point when the current ends, calculating a second state of charge based on the first algorithm; at a third time point after a predetermined time has elapsed from the second time point, calculating a third state of charge and a fifth state of charge respectively based on the first algorithm and the second algorithm; and predicting the state of health (SOH) of the battery based on a first state-of-charge change amount that is the difference between the first state of charge and the second state of charge, a third state-of-charge change amount that is the difference between the first state of charge and the third state of charge, and a fourth state-of-charge change amount that is the difference between the fourth state of charge and the fifth state of charge.

[0022] When predicting the state of health of the battery, a first value can be calculated by applying a first weight to the third state-of-charge change amount, a second value can be calculated by applying a second weight to the fourth state-of-charge change amount, a second state-of-charge change amount can be calculated by adding the first value and the second value to each other, and the state of health of the battery can be predicted based on the first state-of-charge change amount and the second state-of-charge change amount.

[0023] The state of health of the battery can be predicted according to the following equation:

[0024]

[0025] (Here, SOC1_a indicates the first state of charge, SOC1_b indicates the second state of charge, SOC1_c indicates the third state of charge, SOC2_a indicates the fourth state of charge, SOC2_c indicates the fifth state of charge, α indicates the first weight, and β indicates the second weight).

[0026] The first algorithm can predict the state of charge of the battery based on the integral value of the current flowing through the battery during current flow.

[0027] The second algorithm can predict the state of charge of the battery based on the relationship between the open-circuit voltage and the state of charge corresponding to the open-circuit voltage.

[0028]

Beneficial effects

[0029] The present disclosure can predict the state of health (SOH) of a battery with high reliability by applying multiple prediction models and multiple weights.

[0030] The present disclosure can increase the reliability of the predicted state of health (SOH) of a battery by calculating a predicted time point of the state of charge (SOC) of the battery based on a prediction model of battery characteristics and the state of charge (SOC) (which is the basis for calculating the state of health (SOH) of the battery).

[0031] The present disclosure can increase the reliability of the prediction result by predicting the state of health (SOH) of the battery by reflecting weights optimized for the environment in which the battery is used (i.e., the upper-level system in which the battery system is installed) into multiple prediction models.

[0032] The present disclosure can increase the reliability of the prediction result by predicting the state of health (SOH) of the battery by reflecting weights that take into account not only the upper-level system in which the battery system is installed but also the current battery state (e.g., cell voltage) into multiple prediction models. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a conceptual diagram showing an upper-level system in which a battery system is installed according to an embodiment.

[0034] Figure 2 is a detailed illustration of Figure 1 the battery system.

[0035] Figure 3 is a detailed illustration of Figure 2 the information stored in the storage unit.

[0036] Figure 4 is a view showing the change in the state of charge (SOC) of the battery in the discharge mode according to an embodiment.

[0037] Figure 5 is a flowchart explaining a battery state prediction method according to another embodiment. DETAILED DESCRIPTION

[0038] Hereinafter, embodiments disclosed in the specification will be described in detail with reference to the accompanying drawings. Components that are the same or similar are denoted by the same or similar reference numerals, and their repeated description will be omitted. The terms "module" and / or "unit" used for components in the following description are only for facilitating the understanding of the specification. Therefore, these terms do not have meanings or functions that distinguish each other by themselves. In addition, when describing embodiments disclosed in this specification, detailed descriptions of cases where detailed descriptions of known technologies related to the present disclosure may obscure the main points are omitted. In addition, it should be understood that the accompanying drawings are provided only to allow the embodiments of the present disclosure to be easily understood, and the spirit of the present disclosure is not limited by the accompanying drawings and includes all modifications, equivalents, and substitutions included in the spirit and scope of the present disclosure.

[0039] Terms including ordinal numbers such as "first" and "second" may be used to describe various components. However, these components are not limited by these terms. These terms are only used to distinguish one component from another.

[0040] It should be understood that when a component is referred to as being "connected to" or "coupled to" another component, a component may be directly connected or coupled to another component, or may be connected or coupled to another component while having a third component inserted therebetween. On the other hand, it should be understood that when referred to as being "directly connected to" or "directly coupled to" another element, an element may be connected or coupled to another element without a third element being inserted therebetween.

[0041] It should also be understood that the terms "comprising" or "having" used in the specification specify the presence of the features, numbers, steps, operations, components, parts, or combinations thereof mentioned in the specification, and do not exclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0042] Figure 1 is a conceptual diagram showing an upper-layer system equipped with a battery system according to an embodiment; Figure 2 is shown in detail Figure 1 of the battery system; and, Figure 3 is shown in detail Figure 2 of the information stored in the storage unit.

[0043] Referring to Figure 1 an upper-layer system 1 may be equipped with a battery system 2.

[0044] The upper-layer system 1 may include all systems each equipped with a battery. For example, the upper-layer system 1 may include an automobile, a bicycle, an energy storage system (ESS), etc.

[0045] The battery system 2 may include a customized State of X (SOX) prediction algorithm in the upper-level system 1. According to an embodiment, the battery system 2 may identify the upper-level system 1 in which the battery system 2 is currently installed and predict the state of health (SOH) of the battery based on the corresponding state of health (SOH) prediction algorithm.

[0046] Then, the battery system 2 may predict the state of health (SOH) of the battery, and even when a standard battery that can be used in various upper-level systems 1 is installed and used in each upper-level system 1, the state of health can fully reflect the characteristics of the individual upper-level system 1.

[0047] Referring Figure 2 , the battery system 2 may include a battery 10, a repeater 20, a current sensor 30, and a battery management system (BMS) 40.

[0048] The battery 10 may include a plurality of battery cells electrically connected in series and in parallel with each other. In some embodiments, the battery cells may be rechargeable secondary batteries. A predetermined number of battery cells may be connected in series with each other to form a battery module, a predetermined number of battery modules may be connected in series with each other to form a battery pack, and a predetermined number of battery packs may be connected in parallel with each other to form a battery bank, thereby supplying the required power. Figure 1 The battery 10 is shown in which a plurality of battery cells are connected in series with each other, but the battery 10 is not limited thereto and may be configured as a battery module unit, a battery pack unit, or a battery bank unit.

[0049] Each of the plurality of battery cells may be electrically connected to the BMS 40 through wiring. The BMS 40 may collect and analyze various information about the plurality of battery cells to thereby control the charging, discharging, protection operations, etc. of the battery cells and control the operation of the repeater 20.

[0050] Figure 2 The battery 10 is shown including a plurality of battery cells connected in series with each other. The battery 10 is connected between two output terminals OUT1 and OUT2 of the battery system 2. The repeater 20 is connected between the positive electrode of the battery system 2 and the first output terminal OUT1, and the current sensor 30 is connected between the negative electrode of the battery system 2 and the second output terminal OUT2. As Figure 1 shown, the components and the connection relationships between the components are examples, and the present disclosure is not limited thereto.

[0051] The repeater 20 can control the electrical connection between the battery system 2 and an external device. When the repeater 20 is turned on, the battery system 2 and the external device can be electrically connected to each other to thereby perform charging or discharging of the battery, while when the repeater 20 is turned off, the battery system 2 and the external device can be electrically disconnected from each other. Here, the external device can be a charger during a charging cycle in which the battery 10 receives power to thereby be charged, and can be a load during a discharging cycle in which the battery 10 discharges power to the external device.

[0052] The current sensor 30 can be serially connected to the current path between the battery 10 and the external device. The current sensor 30 can measure the battery current flowing through the battery 10, i.e., the charging current or the discharging current, and transmit the measurement result to the BMS 40. For example, the battery current can correspond to the cell current when a plurality of battery cells are connected in series with each other.

[0053] The BMS 40 can include a monitoring unit 41, a storage unit 43, a communication unit 45, and a control unit 47.

[0054] The monitoring unit 41 can be electrically connected to the positive electrode and the negative electrode of each of the plurality of battery cells, and measure 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 can transmit information about the measured cell voltage, battery current, and battery temperature to the control unit 47.

[0055] For example, the monitoring unit 41 can measure the cell voltage of each of the plurality of battery cells at each predetermined interval during a rest period in which no charging or discharging occurs, and calculate the cell current based on the measured cell voltage. The monitoring unit 41 can transmit the cell voltage and the cell current of each of the plurality of battery cells to the control unit 47.

[0056] The storage unit 43 can store system identification information (APP ID), weights, a plurality of prediction models for predicting the battery state (or X state (SOX)), and battery information. The battery state (or X state (SOX)) can include the state of health (SOH) of the battery 10. Here, the battery information can include information related to the battery, such as cell voltage, cell current, battery current, and battery temperature.

[0057] Refer to Figure 3 , the storage unit 43 can store a plurality of SOC prediction models SOC1 and SOC2 for predicting the state of charge (SOC) of the battery 10 based on the battery information by using a predetermined algorithm, at least one SOH prediction model for predicting the state of health (SOH) of the battery 10, and a weight look-up table. Figure 3Shows a first SOC prediction model and a second SOC prediction model, that is, two SOC prediction models. However, the BMS 40 is not limited thereto and may include three or more SOC prediction models. Additionally, Figure 3 Shows a SOH prediction model. However, the BMS 40 is not limited thereto and may include two or more SOH prediction models.

[0058] For example, the storage unit 43 may store a first SOC prediction model for predicting the state of charge (SOC) based on the well-known coulomb counting method, a second SOC prediction model for predicting the state of charge (SOC) based on the open circuit voltage (OCV)-SOC relationship, a third SOC prediction model for predicting the state of charge (SOC) based on the terminal voltage, etc.

[0059] For example, the storage unit 43 may store a first SOH prediction model for predicting the state of health (SOH) based on the well-known OCV-SOH relationship, a second SOH prediction model for predicting the state of health (SOH) based on the SOC-SOH relationship, a third SOH prediction model for predicting the state of health (SOH) based on the direct current internal resistance (DCIR) of the battery cell, etc.

[0060] The system identification information (APP ID) may be identification information for distinguishing the system in which the battery system 2 is installed. For example, the storage unit 43 may store various system identification information (APP IDs), such as vehicle system identification information (E_Vehicle, APP ID = 001), bicycle system identification information (E_Bike, APP ID = 002), and ESS system identification information (E_ESS, APP ID = 003), etc.

[0061] The weight may be a value preset and stored in the storage unit 43 to predict the customized battery state (SOX) in the upper layer system 1. In some embodiments, the weight may include a plurality of first weights corresponding to the system identification information (APP ID). In another embodiment, the weight may include the system identification information (APP ID) and a plurality of second weights corresponding to a predetermined prediction condition. A more detailed description thereof is provided below by the control unit 47.

[0062] The prediction condition may be a condition reflecting the current state of the battery cell. For example, the prediction condition may be determined based on the result of comparing the cell voltage of the battery cell with a predetermined reference value. For another example, the prediction condition may include conditions determined by the cell voltage, battery current, or battery temperature of the battery cell. However, the prediction condition is not limited to the cell voltage, battery current, or battery temperature, and may include various conditions reflecting the current state of the battery 10 or the current state of the battery cell.

[0063] The communication unit 45 can communicate with the upper-layer system 1 and receive the identification information of the upper-layer system 1 (hereinafter referred to as system identification information). 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.

[0064] The control unit 47 can determine the system identification information (APP ID) and weights corresponding to at least one of the predetermined prediction conditions based on the look-up table. The control unit 47 can apply the determined weights to the multiple prediction results predicted by the multiple SOX prediction models, and calculate the average value by adding the multiple prediction results to which the weights are applied.

[0065] In some embodiments, the control unit 47 can calculate the state of health (SOH) of the battery 10 based on the average value of the multiple SOC values to which the weights are applied. Below, refer to Figure 4 to describe the specific calculation method of the state of health (SOH).

[0066] Table 1 below shows an example of multiple first weights corresponding to a certain system identification information (APP ID). As described above, the first weight can be a weight that only considers the system identification information (APP ID), and the values corresponding to each prediction model can be different, as shown in Table 1 below.

[0067] [Table 1]

[0068]

[0069] For example, referring to Table 1, assume that the upper-layer system 1 is a vehicle system (E_vehicle, APP ID = 001). The control unit 47 can calculate the average value of the state of charge (SOC) of the battery 10 by applying the first weight 0.7 corresponding to the first SOC value predicted by the first SOC prediction model and applying the first weight 0.3 corresponding to the second SOC value predicted by the second SOC prediction model.

[0070] Specifically, the first SOC value predicted by the first SOC prediction model can be 50%, and the second SOC value predicted by the second SOC prediction model can be 54%. In this case, the control unit 47 can calculate the average value of the state of charge (SOC) as 51.2% (50% × 0.7 + 54% × 0.3 = 51.2%).

[0071] Table 2 below shows an example of a plurality of second weights corresponding to a certain system identification information (APP ID) and prediction conditions. As described above, the second weight can be a weight that takes into account both the system identification information (APP ID) and the prediction conditions, and the values corresponding to each prediction model can be different, as shown in Table 2 below.

[0072] [Table 2]

[0073]

[0074] Table 2 shows conditions where the prediction conditions are determined based on the result of comparing the single-cell voltage with a predetermined reference value (e.g., 3.7V). However, as described above, the prediction conditions are not limited to the single-cell voltage or the reference value, and can be equally applied to the cells of the battery 10. Here, the single-cell voltage can indicate the average value of the single-cell voltages of each of the plurality of battery cells included in the battery module, and is not limited thereto, and can indicate the median or average value of the plurality of single-cell voltages.

[0075] For example, referring to Table 2, assume that the upper-layer system 1 is a vehicle system (E_vehicle, APP ID = 001), and the single-cell voltage of the battery cells currently included in the battery 10 is 3.7V or greater. Referring to Table 2, the control unit 47 can calculate the average value of the charge state (SOC) of the battery cells or the battery 10 by applying the second weight 0.7 corresponding to the first SOC value predicted by the first SOC prediction model and applying the second weight 0.3 corresponding to the second SOC value predicted by the second SOC prediction model.

[0076] Specifically, the first SOC value predicted by the first SOC prediction model can be 50%, and the second SOC value predicted by the second SOC prediction model can be 54%. In this case, the control unit 47 can calculate the average value of the charge state (SOC) of the battery cells or the battery 10 as 51.2% (50% × 0.7 + 54% × 0.3 = 51.2%).

[0077] For another example, referring to Table 2, assume that the upper-layer system 1 is a bicycle system (E_bicycle, APP ID = 002), and the single-cell voltage of the cells currently included in the battery 10 is less than 3.7V. Referring to Table 2, the control unit 47 can calculate the average value of the charge state (SOC) of the battery cells or the battery 10 by applying the second weight 0.5 corresponding to the first SOC value predicted by the first SOC prediction model and applying the second weight 0.5 corresponding to the second SOC value predicted by the second SOC prediction model.

[0078] Specifically, the first SOC value predicted by the first SOC prediction model may be 50%, and the second SOC value predicted by the second SOC prediction model may be 54%. In this case, the control unit 47 may calculate the average value of the state of charge (SOC) of the battery cell or the battery 10 as 52% (50% × 0.5 + 54% × 0.5 = 52%).

[0079] Referring Figure 3 , Tables 1 and 2 may be examples of a weight look-up table. However, the weight look-up table is not limited thereto and may be written in various formats. The above Tables 1 and 2 only describe the state of charge (SOC) of the battery 10 and are not limited thereto. In some embodiments, the control unit 47 may predict 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 method. Below, referring Figure 4 A method by which the control unit 47 predicts the state of health (SOH) of the battery 10 will be described in detail.

[0080] Figure 4 is a view showing a change in the state of charge (SOC) of a battery in a discharge mode according to an embodiment.

[0081] Figure 4 is a graph showing a 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. The first graph A may be a graph showing a change in the state of charge (SOC) of the battery 10 predicted using only one algorithm. The second graph B may be a graph showing a change in the state of charge (SOC) of the battery 10 predicted using multiple algorithms.

[0082] For example, the second graph B may be a graph that better reflects the current state of the battery 10 than the first graph A. Figure 4 The first graph A is shown as a solid line and the second graph B is shown as a dashed line. Hereinafter, in some embodiments, a method for calculating the state of health (SOH) of the battery 10 will be described based on the first curve A.

[0083] The first time point Ta may be the time point when the current of the battery 10 starts to flow or a predetermined time point in a period before the current starts to flow. The second time point Tb may be the time point when the current of the battery 10 ends. The third time point Tc may be the time point when a predetermined standby time T_th has elapsed from the second time point Tb.

[0084] The standby time T_th can be determined experimentally for various reasons such as algorithms for predicting the state of charge (SOC) and / or characteristics of the battery 10. For example, in order to measure the accurate open circuit voltage (OCV), it is necessary to measure the OCV after a predetermined time has elapsed after the current flow in the battery 10 has ended and the state of the battery 10 has thus stabilized. Refer to Figure 4 , when including an algorithm for predicting the state of charge (SOC) based on the OCV, the SOC prediction model can predict the state of charge (SOC) based on the open circuit voltage (OCV) measured at a third time point Tc after a predetermined time has elapsed after the current flow in the battery 10 has ended. In this case, compared with the case of predicting the state of charge (SOC) based on the open circuit voltage (OCV) measured at a second time point Tb when the current flow in the battery 10 has ended, the SOC prediction model can predict the state of charge (SOC) with higher accuracy. In some embodiments, the standby time T_th can be set by considering the time taken to stabilize the battery 10 after the current flow ends based on the type of the battery 10.

[0085] The control unit 47 can calculate the state of health (SOH) of the battery 10 based on the following equations 1 to 3. For example, the control unit 47 can calculate the state of health (SOH) of the battery 10 based on an algorithm (or SOH prediction model) corresponding to the following equations 1 to 3.

[0086] - Equation 1

[0087] - Equation 2

[0088] - Equation 3

[0089] Referring to Equation 1, the first state of charge change amount △SOC_ab can be the state of charge change amount of the battery 10 during the period from the first time point Ta to the second time point Tb. The second state of charge change amount △SOC_ac can be the state of charge change amount of the battery 10 during the period from the first time point Ta to the third time point 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 △SOC_ab to the second state of charge change amount △SOC_ac.

[0090] Equation 2 can be a more detailed expression of Equation 1. Equation 3 can be a more detailed expression of Equation 2. Referring to Equation 2 and Equation 3, the state of charge SOC1 can be the state of charge (SOC) calculated based on the first SOC prediction model. The state of charge SOC2 can be the state of charge (SOC) calculated based on the second SOC prediction model.

[0091] In some embodiments, the first SOC prediction model may be an algorithm for predicting the state of charge (SOC) based on the Coulomb counting method. The Coulomb counting method may be an algorithm for predicting the state of charge (OSC) of the battery 10 based on the integrated value of the current flowing through the battery 10 during current flow. For example, the Coulomb counting method may correspond to Equation 4 below.

[0092] - Equation 4

[0093] Equation 4 shows that SOC(t) is the state of charge (SOC) at a predetermined time t, SOC(0) is the initial state of charge (at t = 0), 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 prediction model is not limited to Equation 4 and may include various existing algorithms for predicting the state of charge (SOC) of the battery 10 by accumulating the battery current flowing through the battery 10 during the charging or discharging process.

[0094] The second SOC prediction model may be an algorithm for predicting the state of charge (SOC) based on an "OCV-SOC relationship table" or an "OCV-SOC relationship graph" showing the relationship between the state of charge (SOC) and the corresponding open circuit voltage (OCV). For example, the OCV-SOC relationship graph is a graph showing the state of charge (SOC) on the horizontal axis and the open circuit voltage (OCV) on the vertical axis, and can be calculated through experiments. When a predetermined open circuit voltage (OCV) value is obtained, the corresponding state of charge (SOC) can be easily confirmed.

[0095] Referring to Equation 2, Equation 3, and Equation 4, the control unit 47 may calculate the first state of charge change amount △SOC1_ab. Specifically, the control unit 47 may calculate the first state of charge SOC1_a corresponding to the first time point Ta based on the first SOC prediction model, and calculate the second state of charge SOC1_b corresponding to the second time point Tb based on the first SOC prediction model. The control unit 47 may calculate the first state of charge change amount △SOC1_ab corresponding to the difference between the first state of charge SOC1_a and the second state of charge SOC1_b.

[0096] The first state of charge change amount △SOC1_ab may be the state of charge change amount of the battery 10 calculated based on the first SOC prediction model during the period from the first time point Ta to the second time point Tb. Additionally, referring to Equation 1 and Equation 2, the first state of charge change amount △SOC_ab may correspond to the first state of charge change amount △SOC1_ab.

[0097] The control unit 47 may calculate a third charge state change amount ΔSOC1_ac. Specifically, the control unit 47 may calculate a third charge state SOC1_c corresponding to the third time point Tc based on the first SOC prediction model. The control unit 47 may calculate a third charge state change amount ΔSOC1_ac corresponding to the difference between the first charge state SOC1_a and the third charge state SOC1_c.

[0098] The control unit 47 may calculate a fourth charge state change amount ΔSOC2_ac. Specifically, the control unit 47 may calculate a fourth charge state SOC2_a corresponding to the first time point Ta based on the second SOC prediction model. The control unit 47 may calculate a fifth charge state SOC2_c corresponding to the third time point Tc based on the second SOC prediction model. The control unit 47 may calculate a fourth charge state change amount ΔSOC2_ac corresponding to the difference between the fourth charge state SOC2_a and the fifth charge state SOC2_c.

[0099] The control unit 47 may calculate a first denominator value α(ΔSOC1_ac) by applying a first weight α to the third charge state change amount ΔSOC1_ac. The control unit 47 may calculate a second denominator value β(ΔSOC2_ac) by applying a second weight β to the fourth charge state change amount ΔSOC2_ac. The control unit 47 may calculate a fifth charge state change amount α(ΔSOC1_ac)+β(ΔSOC2_ac) by adding the first denominator value α(ΔSOC1_ac) and the second denominator value β(ΔSOC2_ac).

[0100] The fifth charge state change amount α(ΔSOC1_ac)+β(ΔSOC2_ac) may correspond to the charge state 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 prediction model and the second SOC prediction model. Additionally, referring to Equation 1 and Equation 3, the second charge state change amount ΔSOC_ac may correspond to the fifth charge state change amount α(ΔSOC1_ac)+β(ΔSOC2_ac).

[0101] The control unit 47 may calculate the state of health (SOH) of the battery 10 based on the ratio of the first charge state change amount ΔSOC_ab to the fifth charge state change amount α(ΔSOC1_ac)+β(ΔSOC2_ac).

[0102] Figure 5 is a flowchart explaining a battery state prediction method according to another embodiment.

[0103] Referring to Figure 4 and Figure 5, before a current flow between the battery 10 and an external device starts at a predetermined first time point Ta, the battery management system (BMS) 40 can calculate 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 for predicting the state of charge (SOC) of the battery 10, respectively.

[0104] In some embodiments, the first algorithm can be an algorithm corresponding to the Coulomb counting method for predicting the state of charge of the battery 10 based on the integrated value of the current flowing through the battery 10 during the current flow. For example, the Coulomb counting method can correspond to Equation 4 above.

[0105] In some embodiments, the first algorithm can be an algorithm for predicting the state of charge of the battery 10 based on an "open circuit voltage (OCV)-state of charge (SOC) relationship table" or an "OCV-SOC relationship diagram" showing the relationship between the state of charge (SOC) and the corresponding open circuit voltage (OCV).

[0106] Next, the BMS 40 can calculate a second state of charge SOC1_b (S200) based on the first algorithm at a second time point Tb when the current flow ends.

[0107] Next, the BMS 40 can calculate a third state of charge SOC1_c and a fifth state of charge SOC2_c (S300) based on the first algorithm and the second algorithm, respectively, at a third time point Tc when a predetermined time has elapsed since the second time point Tb.

[0108] The third time point Tc can be a time point when a predetermined standby time T_th has elapsed since the second time point Tb. The standby time T_th can be determined experimentally for various reasons such as algorithms for predicting the state of charge (SOC) and / or characteristics of the battery 10. For example, in order to measure an accurate open circuit voltage (OCV), it is necessary to measure the OCV after a predetermined time has elapsed after the current flow of the battery 10 ends and the state of the battery 10 has thus stabilized. Refer to Figure 4 , when including an algorithm for predicting the state of charge (SOC) based on OCV, the SOC prediction model can predict the state of charge (SOC) based on the open circuit voltage (OCV) measured at the third time point Tc when a predetermined time has elapsed after the current flow of the battery 10 ends. In this case, compared with the case of predicting the state of charge (SOC) based on the open circuit voltage (OCV) measured at the second time point Tb when the current flow of the battery 10 ends, the SOC prediction model can predict the state of charge (SOC) with higher accuracy. In some embodiments, the standby time T_th can be set by considering the time taken to stabilize the battery 10 after the current flow ends based on the type of the battery 10.

[0109] Next, the BMS 40 can predict the state of health (SOH) of the battery based on the first to fifth state of charge SOC1_a, SOC1_b, SOC1_c, SOC2_a, and SOC2_c (S400).

[0110] The BMS 40 can calculate a first state of charge change amount ΔSOC_ab that is the difference between the first state of charge SOC1_a and the second state of charge SOC1_b. Specifically, referring to Equation 1 above, the first state of charge change amount ΔSOC_ab can be the state of charge change amount of the battery 10 predicted based on the first algorithm during the period from the first time point Ta to the second time point Tb.

[0111] The BMS 40 can calculate a third state of charge change amount ΔSOC1_ac that is the difference between the first state of charge SOC1_a and the third state of charge SOC1_c. Specifically, referring to Equation 2 above, the third state of charge change amount ΔSOC1_ac can be the state of charge change amount of the battery 10 predicted based on the first algorithm during the period from the first time point Ta to the third time point Tc.

[0112] The BMS 40 can calculate a fourth state of charge change amount ΔSOC2_ac that is the difference between the fourth state of charge SOC2_a and the fifth state of charge SOC2_c. Specifically, referring to Equation 2 above, the fourth state of charge change amount ΔSOC2_ac can be the state of charge change amount of the battery 10 predicted based on the second algorithm during the period from the first time point Ta to the third time point Tc.

[0113] The BMS 40 can calculate 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 Equation 2, Equation 3, and Equation 4, the second state of charge change amount ΔSOC_ac can be the state of charge change amount of the battery 10 predicted based on the first algorithm and the second algorithm during the period from the first time point Ta to the third time point Tc.

[0114] Specifically, the BMS 40 can calculate a first value α(ΔSOC1_ac) by applying a first weight α to the third state of charge change amount ΔSOC1_ac, and calculate a second value β(ΔSOC2_ac) by applying a second weight β to the fourth state of charge change amount (ΔSOC2_ac). The BMS 40 can calculate the second state of charge change amount ΔSOC_ac by adding the first value and the second value to each other.

[0115] The BMS 40 can predict the state of health (SOH) of the battery 10 based on the first state of charge change ΔSOC_ab and the second state of charge change ΔSOC_ac. Referring to Equation 1, the BMS 40 can predict the state of health (SOH) of the battery 10 based on the ratio of the first state of charge change ΔSOC_ab to the second state of charge change ΔSOC_ac.

[0116] Although the embodiments of the present disclosure have been described in detail above, the scope of the present disclosure is not limited thereto. Various modifications and improvements made by those skilled in the art to which the present disclosure pertains also fall within the scope of the present disclosure.

Claims

1. A battery system, comprising: a battery including a plurality of battery cells; and a battery management system (BMS) that calculates a first state of charge change amount, the first state of charge change amount being a difference between a first state of charge (SOC) and a second state of charge, the first state of charge being calculated at a predetermined first time point before the start of current flow between the battery and an external device, and the second state of charge being calculated at a second time point when the current flow ends, and the BMS calculates a second state of charge change amount, the second state of charge change amount being a difference between the first state of charge and a third state of charge, the third state of charge being calculated at a third time point after a predetermined time has elapsed from the second time point, wherein the BMS predicts a state of health (SOH) of the battery based on the first state of charge change amount and the second state of charge change amount.

2. The system according to claim 1, wherein the BMS calculates the first state of charge change amount based on a predetermined first algorithm for predicting the state of charge of the battery, and calculates the second state of charge change amount based on the first algorithm and a predetermined second algorithm for predicting the state of charge of the battery.

3. The system according to claim 2, wherein 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 calculated based on the first algorithm and the third state of charge, calculates a second value 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 based on the second algorithm and a fifth state of charge calculated at the third time point, and calculates the second state of charge change amount by adding the first value and the second value to each other.

4. The system according to claim 3, wherein the BMS predicts the state of health of the battery based on the following equation: Here, SOC1_a indicates the first state of charge, SOC1_b indicates the second state of charge, SOC1_c indicates the third state of charge, SOC2_a indicates the fourth state of charge, SOC2_c indicates the fifth state of charge, α indicates the first weight, and β indicates the second weight.

5. The system according to claim 3, wherein the first algorithm predicts the state of charge of the battery based on an integrated value of the current flowing through the battery during the current flow.

6. The system according to claim 3, wherein the second algorithm predicts the state of charge of the battery based on a relationship between an open circuit voltage and the state of charge corresponding to the open circuit voltage.

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

8. A method for predicting a battery state, comprising: At a predetermined first time point before the start of current flow between the battery and the external device, a first state of charge and a fourth state of charge are respectively calculated based on a predetermined first algorithm and a predetermined second algorithm for predicting the state of charge (SOC) of the battery; At a second time point when the current ends, a second state of charge is calculated based on the first algorithm; At a third time point after a predetermined time has elapsed from the second time point, a third state of charge and a fifth state of charge are respectively calculated based on the first algorithm and the second algorithm; and Based on a first state of charge change amount that is the difference between the first state of charge and the second state of charge, a third state of charge change amount that is the difference between the first state of charge and the third state of charge, and a fourth state of charge change amount that is the difference between the fourth state of charge and the fifth state of charge, the state of health (SOH) of the battery is predicted.

9. The method according to claim 8, wherein When predicting the state of health of the battery, A first value is calculated by applying a first weight to the third state of charge change amount, a second value is calculated by applying a second weight to the fourth state of charge change amount, a second state of charge change amount is calculated by adding the first value and the second value to each other, and the state of health of the battery is predicted based on the first state of charge change amount and the second state of charge change amount.

10. The method according to claim 9, wherein When predicting the state of health of the battery, The state of health of the battery is predicted based on the following equation: Here, SOC1_a indicates the first state of charge, SOC1_b indicates the second state of charge, SOC1_c indicates the third state of charge, SOC2_a indicates the fourth state of charge, SOC2_c indicates the fifth state of charge, α indicates the first weight, and β indicates the second weight.

11. The method according to claim 8, wherein The first algorithm predicts the state of charge of the battery based on the integrated value of the current flowing through the battery during the current flow.

12. The method according to claim 8, wherein The second algorithm predicts the state of charge of the battery based on the relationship between the open circuit voltage and the state of charge corresponding to the open circuit voltage.

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