Battery management device and method of operation thereof

Abstract

The battery management device 200 according to one embodiment disclosed in this document may include a data management unit that generates life change information for each time interval of the battery based on change information of the battery's life (SOH, State Of Health), and a controller that predicts the end of life (EOL, End Of Life) of the battery for each time interval based on the life change information of the battery for each time interval.

Description

[Technical field]

[0001] The embodiments disclosed herein claim the benefit of priority based on Korean Patent Application No. 10-2022-0016540, filed on February 8, 2022, and all contents disclosed in the documents of the Korean patent application are incorporated by reference herein. SUMMARY OF THE DISCLOSURE The embodiments disclosed herein relate to a battery management apparatus and method of operation. [Background technology]

[0002] Electric vehicles obtain power by charging batteries with electricity from an external source and then driving a motor with the voltage charged in the battery. The batteries of electric vehicles may generate heat due to chemical reactions that occur during the process of charging and discharging electricity, and this heat may damage the performance and lifespan of the battery. Therefore, a Battery Management System (BMS) that monitors the temperature, voltage, and current of the battery is operated to diagnose and control the battery status.

[0003] However, conventional battery management devices have the problem that they calculate a battery's life deterioration rate or SOH deterioration rate corresponding to the total period of use of the battery, and cannot identify changes in the battery deterioration rate or SOH that occur due to changes in a user's driving pattern or events that occur in the vehicle in real time. Summary of the Invention [Problem to be solved by the invention]

[0004] One objective of the embodiments disclosed herein is to provide a battery management device and an operating method thereof that can analyze a battery's SOH degradation rate by time interval and provide information on the predicted end of battery life for multiple time intervals reflecting the latest vehicle operation history.

[0005] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0006] A battery management device according to one embodiment disclosed in this document may include a data management unit that generates life change information for each time interval of the battery based on change information of the battery's life (SOH, State Of Health), and a controller that processes the life change information for each time interval of the battery and predicts the end of life (EOL, End Of Life) of the battery for each time interval.

[0007] According to an embodiment, the battery may further include a communication unit configured to transmit life change information of the battery for each time period and a life end point of the battery for each time period to another device in a vehicle in which the battery is mounted.

[0008] According to one embodiment, the data management unit can generate information on changes in the life of the battery over time based on at least one of the voltage, current, and temperature of the battery, or status information of a battery pack in which the battery is installed.

[0009] According to one embodiment, the data management unit can generate at least one of a short-term lifespan change pattern, a mid-term lifespan change pattern, and a long-term lifespan change pattern of the battery based on the battery lifespan change information over time.

[0010] According to one embodiment, the data management unit may calculate a simple average value of the battery's life change information for each time interval, or may input the battery's life change information to one of a moving average filter (MAF) or a recursive weighted filter to generate the battery's life change information for each time interval.

[0011] According to one embodiment, the controller can obtain operation information of a vehicle in which the battery is installed, and predict the end of life of the battery for each time period based on the latest operation history of the vehicle and life change information of the battery for each time period.

[0012] According to one embodiment, the controller may predict a life end point of the battery for each time interval corresponding to at least one of a short-term life change pattern, a medium-term life change pattern, and a long-term life change pattern of the battery.

[0013] According to one embodiment, when the vehicle repeatedly travels along the same route, the controller generates life change information of the battery for each time interval for each number of times the same route is repeated, and the communication unit can transmit the life change information of the battery for each time interval for each number of times the same route is repeated to the other device.

[0014] According to one embodiment, when the controller extracts life change information of the battery where the life of the battery is suddenly decreased from the life change information of the battery by time interval, the controller extracts battery data corresponding to the period when the life of the battery is suddenly decreased, and the communication unit can transmit the battery data corresponding to the period when the life of the battery is suddenly decreased to the other device.

[0015] An operating method of a battery management device according to one embodiment disclosed in this document may include the steps of generating state of health (SOH) change information of the battery over time based on battery data, generating life change information of the battery for each time interval based on the life change information of the battery, and predicting the end of life (EOL) of the battery for each time interval based on the life change information of the battery for each time interval.

[0016] According to an embodiment, the method may further include transmitting life change information of the battery for each time period and a life end point of the battery for each time period to another device of the vehicle in which the battery is installed.

[0017] According to one embodiment, the step of generating life change information for each time interval of the battery based on the life change information of the battery may generate at least one of a short-term life change pattern, a mid-term life change pattern, and a long-term life change pattern of the battery based on the life change information of the battery.

[0018] According to one embodiment, the step of generating life change information of the battery for each time interval based on the life change information of the battery may include calculating a simple average value of the life change information of the battery for each time interval, or inputting the life change information of the battery to one of a moving average filter (MAF) or a recursive weighted filter to generate life change information of the battery for each time interval.

[0019] According to one embodiment, the step of predicting the end of life of the battery for each time interval based on the life change information of the battery for each time interval may involve obtaining operation information of a vehicle in which the battery is mounted, and predicting the end of life of the battery for each time interval based on the latest operation history of the vehicle and the life change information of the battery for each time interval.

[0020] According to one embodiment, the step of predicting the end of life of the battery for each time interval based on the life change information of the battery for each time interval may predict the end of life of the battery for each time interval corresponding to at least one of a short-term life change pattern, a medium-term life change pattern, and a long-term life change pattern of the battery. Effect of the Invention

[0021] According to one embodiment of a battery management device and its operating method disclosed in this document, the SOH deterioration rate of a battery can be analyzed by time interval, and information on the expected end of battery life for multiple time intervals reflecting the latest vehicle operation history can be provided. [Brief description of the drawings]

[0022] [Figure 1] FIG. 1 illustrates a battery pack according to one embodiment disclosed herein. [Diagram 2] 1 is a block diagram showing a configuration of a battery management device according to an embodiment disclosed in this document. [Diagram 3] 1 is a graph showing the SOH change over time for a battery according to an embodiment described herein. [Figure 4] 1 is a flow chart illustrating a method of operation of a battery management device according to one embodiment disclosed herein. [Diagram 5] FIG. 2 is a block diagram showing a hardware configuration of a computing system for implementing a battery management device according to an embodiment disclosed herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Some embodiments disclosed in this document will be described in detail below with reference to exemplary drawings. When referring to components in each drawing, it should be noted that the same components are referred to by the same reference numerals as much as possible when they are displayed in other drawings. In addition, when describing the embodiments disclosed in this document, if a detailed description of related publicly known configurations or functions is deemed to hinder understanding of the embodiments disclosed in this document, the detailed description will be omitted.

[0024] In describing the components of the embodiments disclosed in this document, terms such as first, second, A, B, (a), (b) and the like may be used. Such terms are merely intended to distinguish the components from other components, and do not limit the essence, order, or procedure of the components. In addition, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong. Terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with the contextual meaning of the relevant art, and should not be interpreted in an ideal or overly formal sense unless expressly defined in this document.

[0025] FIG. 1 is a diagram illustrating a battery pack according to one embodiment disclosed herein. Referring to FIG. 1 , a battery pack 1000 according to one embodiment disclosed in this document may include a battery module 100 , a battery management unit 200 , and a relay 300 .

[0026] The battery module 100 may include a number of battery cells 110, 120, 130, and 140. Although the number of battery cells is shown as four in FIG. 1, the number of battery cells is not limited thereto, and the battery module 100 may include n (n is a natural number equal to or greater than 2) battery cells.

[0027] The battery module 100 can supply power to a target device (not shown). To this end, the battery module 100 can be electrically connected to the target device. Here, the target device can include an electrical, electronic, or mechanical device that operates by receiving power from a battery pack 1000 including a plurality of battery cells 110, 120, 130, and 140, and for example, the target device can be, but is not limited to, an electric vehicle (EV) or an energy storage system (ESS).

[0028] The multiple battery cells 110, 120, 130, 140 are basic units of a battery that can be used by charging and discharging electrical energy, and may be, but are not limited to, a lithium ion (Li-ion) battery, a lithium ion polymer (Li-ion polymer) battery, a nickel cadmium (Ni-Cd) battery, a nickel metal hydride (Ni-MH) battery, etc. Meanwhile, although FIG. 1 shows one battery module 100, according to an embodiment, multiple battery modules 100 may be included.

[0029] A battery management system (BMS) 200 can predict the life (SOH, State of Health) of the multiple battery cells 110, 120, 130, 140 based on temperature and voltage data of the multiple battery cells 110, 120, 130, 140. The battery management system 200 can remove noise from the battery data of the multiple battery cells 110, 120, 130, 140 and predict the life (SOH) of the multiple battery cells 110, 120, 130, 140 for each battery temperature and charge / discharge rate based on the data from which the noise has been removed.

[0030] The battery management device 200 can manage and / or control the state and / or operation of the battery module 100. For example, the battery management device 200 can manage and / or control the state and / or operation of the multiple battery cells 110, 120, 130, 140 included in the battery module 100. The battery management device 200 can manage charging and / or discharging of the battery module 100.

[0031] Furthermore, the battery management device 200 can monitor the voltage, current, temperature, etc. of the battery module 100 and / or each of the plurality of battery cells 110, 120, 130, 140 included in the battery module 100. For monitoring by the battery management device, sensors and various measurement modules (not shown) can be further provided in the battery module 100, a charge / discharge path, or any position of the battery module 100. The battery management device 200 can calculate parameters indicating the state of the battery module 100, such as SOC (State of Charge) and SOH, based on the measured values ​​of the monitored voltage, current, temperature, etc.

[0032] The battery management unit 200 can control the operation of the relay 300. For example, the battery management unit 200 can short-circuit the relay 300 to supply power to a target device. In addition, the battery management unit 200 can short-circuit the relay 300 when a charging device is connected to the battery pack 1000.

[0033] The battery management unit 200 can calculate the cell balancing time of each of the multiple battery cells 110, 120, 130, and 140. Here, the cell balancing time can be defined as the time required for balancing the battery cells. For example, the battery management unit 200 can calculate the cell balancing time based on the SOC, battery capacity, and balancing efficiency of each of the multiple battery cells 110, 120, 130, and 140.

[0034] As the period of use or the number of times of use of the battery cells 110, 120, 130, 140 increases, the capacity of the battery cells 110, 120, 130, 140 may decrease, the internal resistance may increase, and various factors of the battery may change. The battery management device 200 may calculate the life of the battery based on data of various factors that change as the battery deteriorates. Specifically, the battery management device 200 may calculate the SOH of the battery cells 110, 120, 130, 140 based on data of various factors that change as the battery cells 110, 120, 130, 140 deteriorate. The SOH is an index that can indicate the health state or life state of the battery in a current state relative to an initial state of the battery. The moment when the SOH reaches 0% may be defined as the end of life (EOL). The end of life of the battery may be the point at which the capacity of the battery reaches or falls below the guaranteed capacity.

[0035] For example, the battery management device 200 can calculate the SOH of the multiple battery cells 110, 120, 130, 140 based on at least one factor of the internal resistance, impedance, conductance, capacity, voltage, self-discharge current, charging performance, and number of charge / discharge cycles of the multiple battery cells 110, 120, 130, 140, which change as the multiple battery cells 110, 120, 130, 140 deteriorate.

[0036] The configuration and operation of the battery management unit 200 will be specifically described below. FIG. 2 is a block diagram showing the configuration of a battery life prediction device according to an embodiment disclosed in this document.

[0037] First, referring to FIG. 2, a battery management device 200 may include a data management unit 210, a controller 220, and a communication unit 230. The data management unit 210 can acquire battery data of the multiple battery cells 110, 120, 130, 140 including at least one of the voltage, current, and temperature of the multiple battery cells 110, 120, 130, 140, and status information of the battery pack in which the battery is mounted.

[0038] The data management unit 210 can generate life change information of the multiple battery cells 110, 120, 130, 140 over time based on the battery data of the multiple battery cells 110, 120, 130, 140. Specifically, the data management unit 210 can generate SOH change information of the multiple battery cells 110, 120, 130, 140 over time based on the battery data of the multiple battery cells 110, 120, 130, 140.

[0039] The data management unit 210 processes the life change information of the plurality of battery cells 110, 120, 130, and 140 by time period, and can generate life change information of the plurality of battery cells 110, 120, 130, and 140 by time period.

[0040] For example, the data management unit 210 may calculate a simple average value of the life change information of the plurality of battery cells 110, 120, 130, and 140 for each multiple time interval, and generate life change information for each time interval of the plurality of battery cells 110, 120, 130, and 140. First, the data management unit 210 may remove outliers in order to remove measurement error data or system noise data included in the life change information of the plurality of battery cells 110, 120, 130, and 140.

[0041] For example, the data management unit 210 can input the life change information of the multiple battery cells 110, 120, 130, and 140 to a moving average filter (MAF) to generate life change information for each time interval of the multiple battery cells 110, 120, 130, and 140. Here, the moving average filter is a technique for calculating the average of n adjacent data in continuous data and sequentially filtering the data. Since the moving average filter can calculate the average while moving the data collection range, it has the effect of mitigating errors caused by sudden changes in continuous data.

[0042] Also, for example, the data management unit 210 may input the life change information of the battery cells 110, 120, 130, and 140 to a recursive weighted filter to generate life change information of the battery cells 110, 120, 130, and 140 for each time period.

[0043] According to an embodiment, the data management unit 210 processes lifespan change information of the plurality of battery cells 110, 120, 130, and 140 over time by time interval, and generates at least one of a short-term lifespan change pattern, a mid-term lifespan change pattern, and a long-term lifespan change pattern of the plurality of battery cells 110, 120, 130, and 140. Here, short-term may mean one day, mid-term may mean one week, and long-term may mean one month.

[0044] According to one embodiment, the data management unit 210 processes life change information of the multiple battery cells 110, 120, 130, and 140 over time by time interval, and can generate at least one of a short-term SOH deterioration rate, a medium-term SOH deterioration rate, and a long-term SOH deterioration rate of the multiple battery cells 110, 120, 130, and 140.

[0045] In addition, the controller 220 processes the life change information of the multiple battery cells 110, 120, 130, 140 for each time interval, and can predict the end of life (EOL) of the multiple battery cells 110, 120, 130, 140 for each time interval. Specifically, the controller 220 can predict the end of life of the multiple battery cells 110, 120, 130, 140 for each time interval, based on the initial SOH values ​​of the multiple battery cells 110, 120, 130, 140 and the life change patterns of the multiple battery cells 110, 120, 130, 140 for each time interval.

[0046] FIG. 3 is a graph showing the SOH change over time for a battery according to one embodiment described herein. 3, the controller 220 may set the initial SOH values ​​of the battery cells 110, 120, 130, 140 as starting values, and indicate the SOH deterioration rates of the battery cells 110, 120, 130, 140 over time. Specifically, the controller 220 may extract a SOH deterioration graph for each time interval of the battery cells 110, 120, 130, 140 having the initial SOH values ​​of the battery cells 110, 120, 130, 140 as Y-intercept values ​​based on the life change data of the battery cells 110, 120, 130, 140 measured up to the observation time point. The controller 220 may predict the time point at which the Y-axis value of the SOH deterioration graph for each time interval of the battery cells 110, 120, 130, 140 reaches the SOH minimum value as the end of life of the battery cells 110, 120, 130, 140 for each time interval.

[0047] According to an embodiment, the controller 220 may obtain vehicle operation information from another device (not shown) of the vehicle equipped with the battery pack 1000. Here, the vehicle operation information may be defined as a motion input by a user of the vehicle using the vehicle or an electronic device installed in the vehicle and characteristics of the motion. For example, the vehicle operation information may include usage information of the battery pack 1000 while the user is driving the vehicle.

[0048] The controller 220 can predict the end of life of the battery cells 110, 120, 130, 140 for each time period based on the latest operation history information of the vehicle and the life change information for each time period of the battery cells 110, 120, 130, 140. That is, the controller 220 can extract a life change pattern for each time period of the battery cells 110, 120, 130, 140 mounted in the vehicle based on the latest operation history information of the acquired vehicle operation information, to reflect the latest operation pattern of the vehicle.

[0049] The controller 220 may extract a vehicle operation pattern by applying machine learning to the vehicle operation information. According to an embodiment, the controller 220 may generate life change information for each time interval of the plurality of battery cells 110, 120, 130, 140 for each number of times the same route is repeated when the vehicle repeatedly travels along the same route. The controller 220 may compare the life change information for each time interval of the plurality of battery cells 110, 120, 130, 140 for each number of times the same route is repeated.

[0050] The controller 220 can extract life change information of the plurality of battery cells 110, 120, 130, 140 corresponding to a period when the life of the plurality of battery cells 110, 120, 130, 140 suddenly decreases from the life change information of the plurality of battery cells 110, 120, 130, 140 by time period.

[0051] The communication unit 230 can receive information related to vehicle operation from other devices of the vehicle. Here, in one embodiment, the other devices of the vehicle may be an electronic control unit (ECU) of the vehicle. The electronic control unit of the vehicle can communicate with other electronic control units of the vehicle without a host computer within the vehicle via CAN communication.

[0052] The communication unit 230 may transmit at least one of the life change information for each time period of the battery cells 110, 120, 130, 140 and the life end time points for each time period of the battery cells 110, 120, 130, 140 to another device of the vehicle.

[0053] The communication unit 230 may transmit life change information for each time period of the plurality of battery cells 110, 120, 130, and 140 for each number of repetitions of the same path generated by the controller 220 to another device.

[0054] When the controller 220 extracts life change information of the battery cells 110, 120, 130, 140 in which the lifespans of the battery cells 110, 120, 130, 140 are suddenly decreased from the lifespan change information of the battery cells 110, 120, 130, 140 by time period, the communication unit 230 can transmit battery data of the battery cells 110, 120, 130, 140 corresponding to a time when the lifespans suddenly decreased to another device. Specifically, the communication unit 230 can transmit vehicle operation information of the number of times when the SOH degradation rates of the battery cells 110, 120, 130, 140 suddenly decreased or change values ​​of the battery cells 110, 120, 130, 140 to another device of the vehicle.

[0055] As described above, the battery management device 200 according to one embodiment disclosed in this document can analyze the SOH deterioration rate of a battery by time interval and provide information on the expected end of battery life for multiple time intervals reflecting the latest vehicle operation history.

[0056] In addition, the battery management device 200 expresses the final expected life of the vehicle battery in terms of the end of life (EOL) rather than the state of health (SOH), thereby making it possible to improve the intuitiveness of battery management.

[0057] The battery management device 200 can first analyze driving information of a vehicle equipped with a battery, and then generate a battery SOH deterioration graph reflecting the latest driving history of the vehicle, and provide the graph to the vehicle user. Thus, the battery management device 200 can compare and provide the vehicle user with predicted end of life data according to the vehicle driving history, and improve the driving habits and driving patterns of the vehicle user to increase the service life of the battery.

[0058] That is, the battery management device 200 can maximize the performance of the battery by providing the expected end of battery life for each time interval and encouraging active participation of the vehicle user in optimizing the battery life. Also, the battery management device 200 can provide information on the deterioration transition of the battery at any time, allowing the user to easily accept the process of reaching the final end of the battery life.

[0059] FIG. 4 is a diagram illustrating a method of operation of a battery management device according to an embodiment disclosed herein. Referring to FIG. 4, the operating method of the battery management device may include a step (S101) of generating battery state of health (SOH) change information over time based on battery data, a step (S102) of processing the battery state of health change information to generate battery state of health change information for each time interval, and a step (S013) of predicting the end of life (EOL) of the battery for each time interval based on the battery state of health change information for each time interval.

[0060] In step S101, the battery management unit 200 can acquire battery data of the multiple battery cells 110, 120, 130, 140 including at least one of the voltage, current, temperature, or status information of the battery pack in which the battery is mounted.

[0061] In step S101, the data management unit 210 can generate life change information of the multiple battery cells 110, 120, 130, 140 over time based on the battery data of the multiple battery cells 110, 120, 130, 140. Specifically, the data management unit 210 can generate SOH change information of the multiple battery cells 110, 120, 130, 140 over time based on the battery data of the multiple battery cells 110, 120, 130, 140.

[0062] In step S102, the data management unit 210 processes the life change information of the plurality of battery cells 110, 120, 130, 140 by time interval, and can generate life change information of the plurality of battery cells 110, 120, 130, 140 by time interval.

[0063] In step S102, for example, the data management unit 210 may calculate a simple average value of the life change information of the plurality of battery cells 110, 120, 130, and 140 for each certain time interval, and generate life change information for each time interval of the plurality of battery cells 110, 120, 130, and 140. First, the data management unit 210 may remove outliers in order to remove measurement error data or system noise data included in the life change information of the plurality of battery cells 110, 120, 130, and 140.

[0064] In step S102, for example, the data management unit 210 can input the life change information of the multiple battery cells 110, 120, 130, and 140 to a moving average filter (MAF) to generate life change information for each time interval of the multiple battery cells 110, 120, 130, and 140. Here, the moving average filter is a technique for calculating the average of n adjacent data in continuous data and sequentially filtering the data. Since the moving average filter can calculate the average while moving within the data collection range, it has the effect of mitigating errors caused by sudden changes in continuous data.

[0065] In step S102, for example, the data management unit 210 may input the life change information of the battery cells 110, 120, 130, and 140 to a recursive weighted filter to generate life change information of the battery cells 110, 120, 130, and 140 for each time period.

[0066] In step S102, the data management unit 210 processes the life change information of the plurality of battery cells 110, 120, 130, 140 over time by time interval, and can generate at least one of a short-term life change pattern, a mid-term life change pattern, and a long-term life change pattern of the plurality of battery cells 110, 120, 130, 140. Here, the short term can mean one day, the mid-term can mean one week, and the long term can mean one month.

[0067] In step S103, the controller 220 processes the life change information of the multiple battery cells 110, 120, 130, 140 for each time interval, and can predict the end of life (EOL) for each time interval of the multiple battery cells 110, 120, 130, 140. Specifically, in step S103, the controller 220 can predict the end of life for each time interval of the multiple battery cells 110, 120, 130, 140 based on the initial SOH values ​​of the multiple battery cells 110, 120, 130, 140 and the life change patterns of the multiple battery cells 110, 120, 130, 140 for each time interval. In step S103, for example, the controller 220 can predict the end of life of the multiple battery cells 110, 120, 130, 140 for each time interval as the time point at which the Y-axis value of the SOH deterioration graph for each time interval of the multiple battery cells 110, 120, 130, 140 reaches the minimum SOH value.

[0068] In step S103, the controller 220 can predict the end of life for each time period of the multiple battery cells 110, 120, 130, 140 based on the latest operation history information of the vehicle and the life change information for each time period of the multiple battery cells 110, 120, 130, 140. In step S103, that is, the controller 220 can extract a life change pattern for each time period of the multiple battery cells 110, 120, 130, 140 mounted in the vehicle based on the latest operation history information of the acquired vehicle operation information, to reflect the latest operation pattern of the vehicle.

[0069] In step S103, the controller 220 may extract a vehicle operation pattern by applying machine learning to the vehicle operation information. According to an embodiment, the controller 220 may generate life change information for each time interval of the plurality of battery cells 110, 120, 130, 140 for each number of times the same route is repeated when the vehicle repeatedly travels along the same route. In step S103, the controller 220 may compare life change information for each time interval of the plurality of battery cells 110, 120, 130, 140 for each number of times the same route is repeated.

[0070] In step S103, the controller 220 can extract life change information of the multiple battery cells 110, 120, 130, 140 by time period, which corresponds to a period when the life of the multiple battery cells 110, 120, 130, 140 is rapidly decreasing, from the life change information of the multiple battery cells 110, 120, 130, 140 by time period.

[0071] In step S103, the communication unit 230 may transmit at least one of the life change information for each time period of the plurality of battery cells 110, 120, 130, 140 and the life end time points for each time period of the plurality of battery cells 110, 120, 130, 140 to another device of the vehicle.

[0072] In step S103, the communication unit 230 may transmit life change information for each time period of the plurality of battery cells 110, 120, 130, and 140 for each number of repetitions of the same path generated by the controller 220 to another device.

[0073] In step S103, when the controller 220 extracts life change information of the multiple battery cells 110, 120, 130, 140 in which the lifespan of the multiple battery cells 110, 120, 130, 140 is suddenly decreased from the lifespan change information of the multiple battery cells 110, 120, 130, 140 by time interval, the communication unit 230 can transmit battery data of the multiple battery cells 110, 120, 130, 140 corresponding to the period when the lifespan suddenly decreased to another device.

[0074] In step S103, specifically, the communication unit 230 may transmit vehicle operation information regarding the number of times the SOH deterioration rates of the battery cells 110, 120, 130, and 140 suddenly decreased or change values ​​of the battery cells 110, 120, 130, and 140 to other devices in the vehicle.

[0075] FIG. 5 is a block diagram showing a hardware configuration of a computing system that realizes a battery management device according to an embodiment disclosed in this document. Referring to FIG. 5, a computing system 2000 according to one embodiment disclosed in this document may include an MCU 2100, a memory 2200, an input / output I / F 2300, and a communication I / F 2400.

[0076] The MCU 2100 may be a processor that executes various programs (e.g., a battery end-of-life prediction program) stored in the memory 2200, processes various data via such programs, and performs the functions of the battery management device 200 shown in FIG. 1 described above.

[0077] The memory 2200 can store various programs related to the operation of the battery management unit 200. The memory 2200 can also store operation data of the battery management unit 200.

[0078] A plurality of such memories 2200 may be provided as necessary. The memory 2200 may be a volatile memory or a non-volatile memory. The memory 2200 as a volatile memory may be a RAM, a DRAM, an SRAM, or the like. The memory 2200 as a non-volatile memory may be a ROM, a PROM, an EAROM, an EPROM, an EEPROM, a flash memory, or the like. The examples of the memory 2200 listed above are merely illustrative and are not limited to these examples.

[0079] The input / output I / F 2300 can provide an interface that connects input devices (not shown) such as a keyboard, mouse, or touch panel, and output devices such as a display (not shown), to the MCU 2100 to transmit and receive data.

[0080] The communication I / F 2400 is configured to be able to transmit and receive various data to and from a server, and may be any of various devices capable of supporting wired or wireless communication. For example, programs for measuring resistance and diagnosing abnormalities, various data, and the like can be transmitted and received from a separately provided external server via the communication I / F 2400.

[0081] The above description is merely an illustrative example of the technical ideas of the present disclosure, and various modifications and variations can be made by a person having ordinary knowledge in the technical field to which the present disclosure pertains without departing from the essential characteristics of the present disclosure.

[0082] Therefore, the embodiments disclosed in this disclosure are intended to explain the technical idea of ​​the disclosure, not to limit it, and such embodiments do not limit the scope of the technical idea of ​​the disclosure. The scope of protection of the disclosure should be interpreted according to the claims below, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of rights of the disclosure.

Claims

1. a data management unit that generates battery life change information for each time period based on the battery life change information; A controller for predicting a life end point of the battery for each time period based on life change information of the battery for each time period; A battery management device comprising:

2. The battery management device of claim 1 , further comprising a communication unit configured to transmit the life change information of the battery for each time period and the life end time point of the battery for each time period to another device of a vehicle equipped with the battery.

3. The battery management device according to claim 1 , wherein the data management unit generates information on changes in the life of the battery over time based on at least one of the voltage, current, and temperature of the battery, or status information of a battery pack in which the battery is mounted.

4. The battery management device of claim 3 , wherein the data management unit generates at least one of a short-term life change pattern, a medium-term life change pattern, and a long-term life change pattern of the battery based on the battery life change information over time.

5. The battery management device of claim 3, wherein the data management unit calculates a simple average value of the battery's life change information for each time interval, or inputs the battery's life change information into one of a moving average filter or a recursive weighting filter to generate the battery's life change information for each time interval.

6. 5. The battery management device of claim 4, wherein the controller acquires operation information of a vehicle in which the battery is mounted, and predicts the end of life of the battery for each time interval based on the latest operation history of the vehicle and life change information of the battery for each time interval.

7. The battery management device of claim 6 , wherein the controller predicts a life end point of the battery for each time interval corresponding to at least one of a short-term life change pattern, a medium-term life change pattern, and a long-term life change pattern of the battery.

8. The controller generates life change information of the battery for each time interval for each number of times the vehicle travels along the same route repeatedly, The battery management device according to claim 6 , wherein the communication unit transmits life change information for each time interval of the battery for each number of repetitions of the same path to the other device.

9. The controller extracts battery data corresponding to a time when the battery life suddenly decreases from the life change information of the battery by time interval, and The battery management device according to claim 6 , wherein the communication unit transmits the battery data corresponding to the time when the lifespan rapidly decreases to the other device.

10. generating battery life change information over time based on the battery data; generating life change information of the battery for each time period based on the life change information of the battery; predicting a life end point of the battery for each time period based on the life change information of the battery for each time period; A method of operating a battery management device, comprising:

11. The method of claim 10, further comprising the step of transmitting the life change information of the battery for each time period and the life end point of the battery for each time period to another device of a vehicle in which the battery is mounted.

12. The step of generating life change information of the battery for each time period based on the life change information of the battery, The method of claim 10 , further comprising generating at least one of a short-term life variation pattern, a medium-term life variation pattern, and a long-term life variation pattern of the battery based on the life variation information of the battery.

13. The step of generating life change information of the battery for each time period based on the life change information of the battery, The method of claim 12, further comprising: calculating a simple average value of the life change information of the battery for each time interval; or inputting the life change information of the battery into one of a moving average filter or a recursive weighting filter to generate life change information of the battery for each time interval.

14. The step of predicting a life end point of the battery for each time period based on the life change information for each time period of the battery, The method of claim 10, further comprising: acquiring operation information of a vehicle equipped with the battery; and predicting a life end point of the battery for each time period based on the latest operation history of the vehicle and information on changes in life of the battery for each time period.

15. The step of predicting a life end point of the battery for each time period based on the life change information for each time period of the battery, The method of claim 14 , further comprising predicting a life end point of the battery for each time interval corresponding to at least one of a short-term life change pattern, a medium-term life change pattern, and a long-term life change pattern of the battery.

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