Battery state value correction apparatus and method

Abstract

An apparatus for correcting a battery state value according to the present invention can include a state value acquisition module that acquires a state value representing a state of health or a state of deterioration of a target battery over time, a first correction module that removes an abnormal state value from the state value based on a first trend line representing a change trend of the state value over time, and a second correction module that generates a corrected state value of the target battery based on a second trend line representing a change trend of remaining state values after the abnormal state value is removed from the state value over time.

Description

Technical Field

[0001] This application is based on and claims priority to Korean Patent Application No. 10-2024-0169584, filed with the Korean Intellectual Property Office on November 25, 2024, the entire disclosure of which is incorporated herein by reference.

[0002] This disclosure relates to a battery state value correction apparatus and method, and more specifically, to a battery state value correction apparatus and method for correcting state values ​​that represent the health or deterioration state of a battery. Background Technology

[0003] Recently, with the rapid increase in demand for portable electronic products such as laptops, digital cameras and mobile phones, and the commercialization of electric vehicles, energy storage systems, robots and satellites, research on high-performance batteries that can be recharged / discharged and have high energy density has been actively carried out.

[0004] As these batteries undergo repeated charge / discharge cycles, they gradually deteriorate and become unable to maintain their original capacity. Therefore, accurate diagnosis of the battery's health or deterioration status is necessary to ensure the safety and reliability of the battery itself or the devices using it by accurately predicting its usable life, remaining life, and replacement time.

[0005] However, when generating a state value representing the health of a battery based on data collected in a real-world battery usage environment (such as a vehicle) where various noises occur, abnormal state values ​​or state values ​​containing noise may be generated.

[0006] To remove such abnormal state values, the method of simply setting an upper or lower limit for the generated state values ​​and removing state values ​​outside the preset upper or lower limit cannot adaptively respond to changes in battery state that alter the normal range of state values, and has the problem of judging state values ​​corresponding to the normal range as abnormal state values ​​based on the current battery state. Summary of the Invention

[0007] Technical issues

[0008] The technical problem to be solved by this disclosure is to provide a battery state value correction device and method that can adaptively identify and remove abnormal state values ​​based on changes in battery state.

[0009] Another technical problem to be solved by this disclosure is to provide a battery state value correction device and method that can improve the accuracy and reliability of the generated state value even when generating the corresponding battery state value based on data collected in an actual battery use environment where various noises occur.

[0010] Another technical problem to be solved by this disclosure is to provide a battery pack including a battery state value correction device according to this disclosure.

[0011] In addition, the technical problem to be solved by the present invention is to provide a vehicle including a battery state value correction device according to the present disclosure.

[0012] Another technical problem to be solved by this disclosure is to provide a BaaS (Battery as a Service) including a battery state value correction device according to this disclosure.

[0013] Technical solution

[0014] A battery state value correction device according to one aspect of this disclosure includes: a state value acquisition module configured to acquire state values ​​representing the health or deterioration state of a target battery over time; a first correction module configured to remove abnormal state values ​​from the state values ​​based on a first trend line, the first trend line representing the trend of the state values ​​over time; and a second correction module configured to generate corrected state values ​​of the target battery based on a second trend line, the second trend line representing the trend of the remaining state values ​​after removing the abnormal state values ​​over time.

[0015] In one embodiment, the first correction module may be configured to generate the first trend line by mapping the state value to a first orthogonal coordinate system, in which a first axis representing time and a second axis representing the state value are orthogonal.

[0016] In one implementation, the first correction module can be configured to set a normal state value range based on the first trend line, and to determine the state values ​​that do not fall within the normal state value range as the abnormal state values.

[0017] In one embodiment, the first correction module may be configured to send the remaining state value and the cumulative usage time information of the target battery corresponding to the remaining state value to the second correction module.

[0018] In one embodiment, the second correction module may be configured to generate the second trend line by mapping the remaining state values ​​to a second orthogonal coordinate system in which a third axis representing the cumulative usage time of the target battery and a fourth axis representing the state values ​​are orthogonal.

[0019] In one embodiment, the second correction module may be configured to generate a corrected state value of the target battery for each cumulative usage time of the target battery based on the second trend line.

[0020] In one implementation, the second calibration module may be configured to provide a calibrated state value to the BMS (Battery Management System) that manages the target battery.

[0021] In one embodiment, the battery state value correction device may further include a communication unit configured to send the corrected state value to a pre-designated server or communication terminal via a communication network.

[0022] In one embodiment, the battery state value correction device may further include an output unit configured to output the corrected state value.

[0023] According to another aspect of this disclosure, the battery pack includes the aforementioned battery state value correction device.

[0024] According to another aspect of this disclosure, the vehicle includes the aforementioned battery state value correction device.

[0025] A battery state value correction method according to another aspect of this disclosure includes the following steps: a state value acquisition step, the state value acquisition step being executed by a processor, for acquiring state values ​​representing the health state of a target battery over time; a first correction step, the first correction step being executed by the processor, for removing abnormal state values ​​from the state values ​​based on a first trend line, the first trend line representing the trend of the state values ​​over time; and a second correction step, the second correction step being executed by the processor, for generating corrected state values ​​of the target battery based on a second trend line, the second trend line representing the trend of the remaining state values ​​after removing the abnormal state values ​​over time.

[0026] Beneficial effects

[0027] According to this disclosure, abnormal state values ​​included in the state values ​​are removed based on a first trend line representing the time-varying trend of state values ​​representing the health state of the battery, so that the identification and removal of abnormal state values ​​can be adaptively performed according to changes in the battery state.

[0028] Furthermore, a second trend line is generated, which represents the trend of the remaining state values ​​after removing abnormal state values ​​from the state values ​​over time, and a corrected state value for the battery is provided based on the second trend line. Therefore, even when generating the state values ​​of the corresponding battery based on data collected in the actual use environment of the battery where various noises occur, the accuracy and reliability of the generated state values ​​can be improved.

[0029] In addition, the corrected status values ​​are sent to the BMS that manages the battery, a pre-designated server, or a user communication terminal, thereby facilitating the management of the battery pack or vehicle, including the battery, and ensuring safety.

[0030] Furthermore, those skilled in the art to which this disclosure pertains will be able to clearly understand from the following description that various technical problems not mentioned above can be solved according to the various embodiments of this disclosure. Attached Figure Description

[0031] Figure 1 This is a block diagram illustrating a battery state value correction device according to an embodiment of the present disclosure.

[0032] Figure 2 It is a graph showing the state values ​​of a battery generated in a real-world usage environment.

[0033] Figure 3 It is shown Figure 2 The graph shows the first trend line of the state values.

[0034] Figure 4 It shows based on Figure 3 The graph shows the range of normal state values ​​established by the first trend line.

[0035] Figure 5 It shows based on Figure 4 The graph shown is a curve of the normal state value range excluding abnormal state values.

[0036] Figure 6 It is shown Figure 5 The graph shows the second trend line of the normal state value.

[0037] Figure 7 It is a graph comparing the state value correction results according to this disclosure with the state value correction results using the moving average method.

[0038] Figure 8 This is a flowchart illustrating a battery state value correction method according to an embodiment of the present disclosure.

[0039] Figure 9 It is shown Figure 8 The flowchart shows the detailed process of the first correction step.

[0040] Figure 10 It is shown Figure 8 The flowchart shows the detailed process of the second correction step.

[0041] Figure 11 This is a diagram illustrating a battery pack according to an embodiment of the present disclosure.

[0042] Figure 12 This is a diagram showing the vehicle involved in this embodiment. Detailed Implementation

[0043] In the following description, to illustrate the solutions corresponding to the technical challenges of this disclosure, embodiments according to this disclosure will be described in detail with reference to the accompanying drawings. However, when interpreting this disclosure, if the description of related prior art obscures the essence of this disclosure, its description may be omitted. Furthermore, the terminology used in this specification is defined in consideration of the functions in this disclosure, and these terms may vary according to the intentions or practices of the designer, manufacturer, etc. Therefore, the definitions of the terms described below should be based on the entirety of this specification.

[0044] Figure 1 This is a block diagram illustrating a battery state value correction device 100 according to an embodiment of the present disclosure.

[0045] like Figure 1 As shown, the battery state value correction device 100 according to an embodiment of the present disclosure includes a control unit 110. The control unit 110 is configured to correct state values ​​generated for each usage time of a target battery, these state values ​​being state values ​​representing the health or deterioration state of the target battery.

[0046] For this purpose, the control unit 110 may include at least one general-purpose processor or ASIC (Application-Specific Integrated Circuit) for performing battery state value correction logic, and according to one embodiment may optionally further include hardware such as registers and memory.

[0047] Furthermore, the control unit 110 can be configured as a combination of hardware such as a processor and software such as a program. That is, the battery state value correction logic of the control unit 110 can be configured as a program and stored in the control unit 110's own memory or in the storage unit 140 described later, and the stored program can be configured to be executed by the hardware of the control unit 110.

[0048] Meanwhile, the control unit 110 includes a state value acquisition module 112, a first correction module 114, and a second correction module 116, which are detailed components for calibrating the state value of the battery.

[0049] The status value acquisition module 112 is configured to acquire status values ​​representing the health or deterioration status of the target battery over time.

[0050] Here, the target battery can be implemented as a battery cell corresponding to a basic unit for charging and discharging, or as a battery assembly comprising multiple battery cells. The shape and type of the target battery can be varied depending on the implementation method.

[0051] In addition, the state value of the target battery corresponds to a time series data, which represents the health or deterioration state of the target battery based on the cumulative usage time of the target battery, and the type of state value can be changed according to the implementation method.

[0052] In one implementation, the state value is a value representing the State of Health (SOH), which is a relative comparison of the target battery's performance at the Beginning of Life (BOL) with its performance at the current point in time, and can be a value estimated at different times. In another implementation, the state value is a value representing the NP ratio, which is the ratio between the positive and negative electrode capacities of the target battery, and can be a value measured at different times.

[0053] Furthermore, the status value acquisition module 112 can be configured to acquire the corresponding status value from a predetermined device that generates the status value of the target battery. For example, the status value acquisition module 112 can be configured to receive status values ​​from a BMS (Battery Management System) that manages the target battery.

[0054] The first correction module 114 is configured to remove abnormal state values ​​from the state values ​​based on a first trend line representing the trend of state value changes over time.

[0055] In one implementation, the first correction module 114 can be configured to generate a first trend line by mapping state values ​​to a first orthogonal coordinate system, in which a first axis representing time is orthogonal to a second axis representing state values. In this case, the time represented by the first axis can be the time elapsed since the target battery was put into use or the cumulative usage time of the target battery.

[0056] In another embodiment, if the target battery is a battery applied to a vehicle, the first axis of the first orthogonal coordinate system can be replaced by an axis representing the cumulative driving distance of the vehicle.

[0057] For example, the first correction module 114 can use a machine learning model trained to predict trends in time series data to generate a first trend line. In some implementations, the first correction module 114 can use Prophet to generate the first trend line; Prophet is a time series data prediction model from Facebook.

[0058] In one implementation, the first correction module 114 can be configured to set a normal state value range based on a first trend line, and to determine state values ​​that do not fall within the normal state value range as abnormal state values. In this case, the first correction module 114 can set the normal state value range based on the time-based distribution of state values ​​mapped onto a first orthogonal coordinate system.

[0059] Furthermore, in one embodiment, the first correction module 114 may be configured to send the remaining state values ​​in the state values, after removing abnormal state values, and the cumulative usage time information of the target battery corresponding to the remaining state values ​​to the second correction module 116 described below.

[0060] The second correction module 116 is configured to generate the corrected state value of the target battery based on a second trend line representing the trend of the remaining state value sent from the first correction module 114 over time.

[0061] In one implementation, the second correction module 116 may be configured to generate a second trend line by mapping the remaining state values ​​onto a second orthogonal coordinate system, in which a third axis representing the cumulative usage time of the target battery is orthogonal to a fourth axis representing the state values.

[0062] In this scenario, the second correction module 116 can generate a second trend line using a machine learning model trained to predict trends in time series data. In some implementations, the second correction module 116 can use a time series data prediction model such as Prophet to generate the second trend line.

[0063] Additionally, the second correction module 116 can be configured to generate a corrected state value for the target battery for each cumulative usage time of the target battery, based on the second trend line.

[0064] In one implementation, the second calibration module 116 may be configured to provide the calibrated state value of the target battery to the BMS (Battery Management System) that manages the target battery.

[0065] In one embodiment, the second calibration module 116 may be configured to control the communication unit 120, described below, to send the calibrated state value of the target battery to a pre-designated server or communication terminal via a communication network. In this case, the server may be a control server that remotely monitors and controls the state of the target battery or the device to which the target battery is applied. Furthermore, the communication terminal may be a communication terminal used by the user or administrator of the target battery, or the device to which the target battery is applied.

[0066] In one embodiment, the second correction module 116 may be configured to control the output unit 150 described below to output the corrected state value of the target battery as a visual signal and / or an audio signal.

[0067] The status value acquisition module 112, the first correction module 114, and the second correction module 116 of the control unit 110 described above can be implemented as a combination of a processor and a program executed by the processor. In this case, the control unit 110 can be implemented as a single processor or as two or more interconnected processors.

[0068] In one embodiment, the battery state value correction device 100 may further include a communication unit 120. The communication unit 120 may be configured to receive data sent from a remotely located server or communication terminal via a wired and / or wireless communication network, and to send that data to the control unit 110, or to send the state value of a target battery corrected by the control unit 110 to the remotely located server or communication terminal. For this purpose, the communication unit 120 may include a communication modem performing wired and / or wireless communication.

[0069] In one embodiment, the battery state value correction device 100 may further include an input unit 130. The input unit 130 may be configured to receive commands or data from a user or administrator. For this purpose, the input unit 130 may include an input device such as a keyboard, operation buttons, or a touch panel.

[0070] In one embodiment, the battery state value correction device 100 may further include a storage unit 140. The storage unit 140 may be configured to store and manage data required for the operation of the battery state value correction device 100. For this purpose, the storage unit 140 may include at least one of ROM, RAM, EEPROM, registers, flash memory, CD-ROM, magnetic tape, hard disk, floppy disk, and optical data recording devices.

[0071] In one embodiment, the battery state value correction device 100 may further include an output unit 150. The output unit 150 may be configured to output the corrected state value of the target battery generated by the control unit 110 as a visual signal and / or an audio signal. For this purpose, the output unit 150 may include visual output devices such as light-emitting diodes, monitors, display panels, or touchscreens, and audio generation devices such as speakers.

[0072] Figure 2 It is a graph showing the state values ​​of a battery generated in a real-world usage environment.

[0073] like Figure 2As shown, the first correction module 114 can map the state value of the target battery obtained by the state value acquisition module 112 to a first orthogonal coordinate system, in which the X-axis representing time and the Y-axis representing state value are orthogonal. In this case, the time represented by the X-axis can be the time elapsed since the target battery was put into use or the cumulative usage time of the target battery. In addition, the state value of the target battery represented by the Y-axis can be the SOH (State of Health) value.

[0074] As mentioned above, if the target battery is a battery used in a vehicle, the X-axis can be replaced with an axis representing the vehicle's cumulative driving distance.

[0075] After mapping the state value to the first orthogonal coordinates in this way, the first correction module 114 can generate a first trend line representing the trend of the state value changing over time.

[0076] Figure 3 It means Figure 2 The graph shows the first trend line T1 of the state values.

[0077] like Figure 3 As shown, the first correction module 114 can generate a first trend line T1, which represents the trend of the state value mapped onto the first orthogonal coordinate over time.

[0078] In this scenario, the first correction module 114 can use a machine learning model trained to predict trends in time series data to generate a first trend line T1. In one implementation, the first correction module 114 can use Prophet, Facebook's time series data prediction model, to generate the first trend line T1.

[0079] After generating the first trend line T1 in this manner, the first correction module 114 can set the normal state value range based on the first trend line T1.

[0080] Figure 4 It shows based on Figure 3 The graph shows the normal state value range Rn established by the first trend line T1.

[0081] like Figure 4 As shown, the first correction module 114 can set the normal state value range Rn based on the first trend line T1. In this case, the first correction module 114 can set the normal state value range Rn based on the time correlation distribution of the state values ​​mapped onto the first orthogonal coordinates.

[0082] When the normal state value range Rn is set in this way, the first correction module 114 can determine the state value mapped to the first orthogonal coordinate but not falling within the normal state value range Rn as the abnormal state value.

[0083] Next, the first correction module 114 can send the remaining state values ​​in the mapped state values ​​(after removing abnormal state values) and the cumulative usage time information of the target battery corresponding to the remaining state values ​​to the second correction module 116.

[0084] Then, the second correction module 116 can generate the corrected state value of the target battery based on a second trend line representing the trend of the remaining state value over time.

[0085] Figure 5 It shows based on Figure 4 The graph shown is a normal state value range Rn with abnormal state values ​​removed.

[0086] like Figure 5 As shown, the second correction module 116 can map the remaining state values, after removing abnormal state values, onto a second orthogonal coordinate system. In the second orthogonal coordinate system, the X-axis, which represents the cumulative usage time of the target battery, and the Y-axis, which represents the state values, are orthogonal.

[0087] After mapping the remaining state values ​​to the second orthogonal coordinates in this way, the second correction module 116 can generate a second trend line representing the trend of the remaining state values ​​over time.

[0088] Figure 6 It is shown Figure 5 The graph shows the second trend line T2 of the normal state value.

[0089] like Figure 6 As shown, the second correction module 116 can generate a second trend line T2, which represents the trend of the state value mapped onto the second orthogonal coordinate over time.

[0090] In this scenario, the second correction module 116 can use a machine learning model trained to predict trends in time series data to generate a second trend line T2. In some implementations, the second correction module 116 can use Prophet, Facebook's time series data prediction model, to generate the second trend line T2.

[0091] When the second trend line T2 is generated in this manner, the second correction module 116 can generate a corrected state value of the target battery for each cumulative usage time of the target battery based on the second trend line T2.

[0092] For example, the second correction module 116 can determine a point in the second trend line T2 corresponding to the cumulative usage time t, and determine the state value corresponding to that point as the corrected state value of the target battery corresponding to the cumulative usage time t.

[0093] Figure 7 It is a graph comparing the state value correction results according to this disclosure with the state value correction results using the moving average method.

[0094] like Figure 7 As shown, when the present disclosure and the moving average method are applied respectively to correct the state values ​​of the battery (including normal state values ​​and abnormal state values), it can be seen that the second trend line T2 generated according to the present disclosure has smaller fluctuations due to noise than the moving average line MA, and has excellent smoothing performance.

[0095] Figure 8 This is a flowchart illustrating a battery state value correction method according to an embodiment of the present disclosure.

[0096] like Figure 8 As shown, the battery state value correction method according to this disclosure is a method for correcting the state value of a target battery generated based on the usage time of the target battery to a state value representing the health or deterioration state of the target battery. Furthermore, the battery state value correction method according to this disclosure can be executed by a processor. This processor corresponds to a reference... Figure 1 The control unit 110 is described.

[0097] The battery state value correction method according to this disclosure includes a state value acquisition step (S810), a first correction step (S820), and a second correction step (S830).

[0098] First, in the state value acquisition step (S810), the processor acquires a state value representing the health or deterioration state of the target battery over time.

[0099] Here, the target battery can be implemented as a battery cell corresponding to a basic unit for charging and discharging, or as a battery assembly comprising multiple battery cells. The shape and type of the target battery can be varied depending on the implementation method.

[0100] Furthermore, the state value of the target battery corresponds to a time series data, which represents the health or deterioration state of the target battery based on its cumulative usage time. The type of state value can vary depending on the implementation method. For example, the state value can be the SOH value or NP ratio of the target battery, which is estimated or calculated sequentially.

[0101] In this case, the processor can be configured to obtain the state value of the target battery from a predetermined device that generates the corresponding state value. For example, the processor can receive the state value from the BMS (Battery Management System) that manages the target battery.

[0102] Then, in the first correction step (S820), the processor removes abnormal state values ​​from the state values ​​based on a first trend line representing the trend of state value changes over time.

[0103] Then, in the second correction step (S830), the processor generates the corrected state value of the target battery based on a second trend line representing the trend of change of the remaining state values ​​after removing the abnormal state values ​​in the state values ​​over time.

[0104] The processor can repeat the above steps (S810, S820, S830) until a reason for stopping the generation of status values ​​occurs, such as the suspension or replacement of the target battery, user commands, etc. (S840).

[0105] Figure 9 It is shown Figure 8 A flowchart showing the detailed process of the first calibration step (S820).

[0106] like Figure 9 As shown, in the first correction step (S820), the processor can map the obtained state value onto a first orthogonal coordinate system, in which the first axis representing time and the second axis representing the state value are orthogonal (S822).

[0107] In this case, the time indicated by the first axis can be the time elapsed since the target battery was started, or the cumulative usage time of the target battery.

[0108] In another embodiment, if the target battery is a battery applied to a vehicle, the first axis of the first orthogonal coordinate system can be replaced by an axis representing the cumulative driving distance of the vehicle.

[0109] Then, the processor can generate a first trend line (S820) representing the trend of the mapping state values ​​over time.

[0110] In this scenario, the processor can use a machine learning model trained to predict trends in time-series data to generate the first trend line. In some implementations, the processor can use Prophet, a time-series data prediction model from Facebook, to generate the first trend line.

[0111] Next, the processor sets the normal state value range based on the first trend line, identifies the state values ​​that do not fall within the normal state value range as abnormal state values, and removes the corresponding abnormal state values ​​(S826).

[0112] In this case, the processor can set the normal state value range based on the time-dependent distribution of state values ​​mapped to the first orthogonal coordinates.

[0113] Figure 10 It means Figure 8 The flowchart shows the detailed process of the second correction step (S830).

[0114] like Figure 10 As shown, when abnormal state values ​​are removed from the state values ​​mapped to the first orthogonal coordinates, the processor can map the remaining state values, from which the abnormal state values ​​have been removed, to the second orthogonal coordinates, in which the third axis representing time and the fourth axis representing state values ​​are orthogonal (S832).

[0115] In this case, the time represented by the third axis can be the time elapsed since the target battery was first used, or the cumulative usage time of the target battery.

[0116] In another embodiment, if the target battery is a battery applied to a vehicle, the third axis of the second orthogonal coordinate system can be replaced by an axis representing the cumulative driving distance of the vehicle.

[0117] Next, the processor can generate a second trend line (S834) representing the trend of the mapped remaining state values ​​over time.

[0118] In this scenario, the processor can use a machine learning model trained to predict trends in time-series data to generate a second trend line. In some implementations, the processor can use Prophet, a time-series data prediction model from Facebook, to generate the second trend line.

[0119] Then, the processor can generate and provide the corrected state value of the target battery for each cumulative usage time of the target battery based on the second trend line (S836).

[0120] For example, the processor can determine a point on the second trend line corresponding to the cumulative usage time t, and determine the state value corresponding to that point as the corrected state value of the target battery corresponding to the cumulative usage time t.

[0121] In one implementation, the processor can provide the BMS (Battery Management System) that manages the target battery with the corrected state value of the target battery.

[0122] Furthermore, the processor can control the aforementioned communication unit 120 to send the calibrated state value of the target battery to a pre-designated server or communication terminal via a communication network. In this case, the server can be a control server that remotely monitors and controls the state of the target battery or a device using the target battery. Additionally, the communication terminal can be a communication terminal used by the user or administrator of the target battery, or a device using the target battery.

[0123] Furthermore, the processor can be configured to control the aforementioned output unit 150 to output the corrected state value of the target battery as a visual signal and / or an audio signal.

[0124] Figure 11 This is a diagram showing a battery pack 10 according to an embodiment of the present disclosure.

[0125] like Figure 11 As shown, the battery pack 10 according to this disclosure includes references Figure 1 The battery state value correction device 100 and battery 200 are described.

[0126] In one embodiment, the battery pack 10 may optionally further include a measuring device 12, a BMS (battery management system) 14, a charging / discharging device 16, and a cooling device 18.

[0127] The measuring device 12 can be configured to measure the voltage and / or current of the battery 200. For this purpose, the measuring device 12 may include at least one voltage sensor for sensing the voltage of the battery 200 and at least one current sensor for sensing the current of the battery 200. Additionally, the measuring device 12 may include at least one temperature sensor for sensing the temperature of the battery 200.

[0128] BMS 14 can be configured to collect data generated by measuring device 12 to monitor the state of battery 200 and manage the charging and discharging process of the battery. Specifically, BMS 14 can be configured to generate state values ​​indicating the health or deterioration state of battery 200.

[0129] The charging / discharging device 16 can be configured to charge and / or discharge the battery 200. To this end, the charging / discharging device 16 may include a charger for charging the battery 200, a discharger for discharging the battery 200, and at least one switch configured to selectively establish or release an electrical connection between the battery 200 and terminals T1, T2 of the battery pack 10.

[0130] The cooling device 18 can be configured to cool the battery 200. To this end, the cooling device 18 may include at least one of a radiator that absorbs heat from the battery 200 and releases the heat to the outside and a cooler that supplies cooling liquid to the battery 200.

[0131] The battery state value correction device 100 according to the embodiments of the present disclosure can be applied to the battery pack 10 to correct the state value representing the health or deterioration state of the battery 200 included in the battery pack 10.

[0132] Figure 12 This is a diagram showing the vehicle involved in this embodiment.

[0133] like Figure 12 As shown, the vehicle 2 according to an embodiment of the present disclosure includes a battery pack 10 that provides electrical energy required for the operation of the vehicle and a battery state value correction device 100 according to the present disclosure.

[0134] In this case, the battery state value correction device 100 can be configured to be linked to the ECU (electronic control unit) that controls the operation of the BMS of the vehicle 2 or the battery pack 10.

[0135] Additionally, the battery status value correction device 100 can be configured to receive data sent from the remote server 4 via a wired and / or wireless communication network, or to send data generated by the battery status value correction device 100 to the server 4.

[0136] For reference, the battery state value correction device 100 according to this disclosure can be applied to various electrical devices or electrical systems other than vehicles, as well as ESS (energy storage system).

[0137] As described above, according to this disclosure, abnormal state values ​​included in the state values ​​are removed based on a first trend line representing the trend of changes in the state values ​​representing the health state of the battery over time, thereby enabling the identification and removal of abnormal state values ​​to be performed adaptively according to changes in the battery state.

[0138] Furthermore, a second trend line is generated, which represents the trend of the remaining state values ​​(after removing abnormal state values) over time, and the corrected state values ​​of the battery are provided based on this second trend line. Therefore, even when generating the state values ​​of the corresponding battery based on data collected in actual battery usage environments where various noises occur, the accuracy and reliability of the generated state values ​​can be improved.

[0139] In addition, the corrected status value is transmitted to the BMS that manages the battery, a pre-designated server, or a user communication terminal, thereby facilitating the management of the battery pack or vehicle, including the battery, and ensuring safety.

[0140] Furthermore, embodiments of this disclosure can solve various technical problems other than those mentioned in this specification in the corresponding technical field and in related technical fields.

[0141] This disclosure has been described with reference to specific embodiments. However, those skilled in the art will clearly understand that various modified embodiments can be implemented within the technical scope of this disclosure. Therefore, the embodiments disclosed above should be considered illustratively and not restrictively. In other words, the true technical scope of this disclosure is indicated by the claims, and all differences within the scope of their equivalents should be interpreted as including in this disclosure.

[0142] [Explanation of reference numerals in the attached figures]

[0143] 2: Vehicles

[0144] 10: Battery Pack

[0145] 100: Battery Status Value Calibration Equipment

[0146] 110: Control Unit

[0147] 112: Status Value Acquisition Module

[0148] 114: First calibration module

[0149] 116: Second calibration module

[0150] 120: Communication Unit

[0151] 130: Input Unit

[0152] 140: Storage unit

[0153] 150: Output Unit

Claims

1. A battery state value calibration device, the battery state value calibration device comprising: A status value acquisition module is configured to acquire status values ​​representing the health or deterioration state of a target battery over time. A first correction module is configured to remove abnormal state values ​​from the state values ​​based on a first trend line, wherein the first trend line represents the trend of the state values ​​over time. as well as The second correction module is configured to generate the corrected state value of the target battery based on a second trend line, the second trend line representing the trend of change of the remaining state value after removing the abnormal state value over time.

2. The battery state value correction device according to claim 1, in, The first correction module is configured to generate the first trend line by mapping the state value to a first orthogonal coordinate system, in which a first axis representing time and a second axis representing the state value are orthogonal.

3. The battery state value correction device according to claim 2, in, The first correction module is configured to set a normal state value range based on the first trend line, and to determine the state values ​​that do not fall within the normal state value range as the abnormal state values.

4. The battery state value correction device according to claim 1, in, The first correction module is configured to send the remaining state value and the cumulative usage time information of the target battery corresponding to the remaining state value to the second correction module.

5. The battery state value correction device according to claim 1, in, The second correction module is configured to generate the second trend line by mapping the remaining state value to a second orthogonal coordinate system, in which the third axis representing the cumulative usage time of the target battery and the fourth axis representing the state value are orthogonal.

6. The battery state value correction device according to claim 5, in, The second correction module is configured to generate a corrected state value of the target battery for each cumulative usage time of the target battery based on the second trend line.

7. The battery state value correction device according to claim 1, in, The second calibration module is configured to provide calibrated status values ​​to the BMS (Battery Management System) that manages the target battery.

8. The battery state value correction device according to claim 1, wherein the battery state value correction device further comprises: A communication unit configured to send a corrected status value to a pre-designated server or communication terminal via a communication network.

9. The battery state value correction device according to claim 1, wherein the battery state value correction device further comprises: An output unit configured to output the corrected state value.

10. A battery pack comprising a battery state value correction device according to any one of claims 1 to 9 and the target battery.

11. A vehicle comprising a battery state value correction device according to any one of claims 1 to 9 and the target battery.

12. A battery state value correction method, the battery state value correction method comprising the following steps: A state value acquisition step, which is executed by a processor, is used to acquire a state value representing the health status of the target battery over time. A first correction step, executed by the processor, is used to remove abnormal state values ​​from the state values ​​based on a first trend line, wherein the first trend line represents the trend of the state values ​​over time. as well as The second correction step, executed by the processor, is used to generate a corrected state value of the target battery based on a second trend line, the second trend line representing the trend of change of the remaining state value after removing the abnormal state value over time.

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