KR102682629B1 - Method and apparatus for monitoring battery status in real time - Google Patents

Abstract

A system for monitoring the status of a battery in real time is presented. Embodiments include a charging status display unit that displays the charging status of the battery; a temperature sensor that measures the temperature of the battery; an output unit that inputs a constant current to the battery; a measuring unit that measures voltage, current, and impedance of the battery; processor; and a memory, wherein the output unit filters noise of the current transmitted from the battery, removes static electricity applied to the output unit, removes sparks generated by the charge remaining in the capacitor, and performs the measurement. Select a capacitor in response to the voltage measured by the unit, select the frequency of the input current to measure the resistance and reactance of the impedance of the battery, and set the size of the input current in response to the impedance measured by the measurement unit. And, the memory stores the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, the capacity of the battery, the profile for the battery, and a neural network model for predicting the life of the battery, , the measuring unit determines the first impedance by measuring the impedance of the battery at a first time, and determines the second impedance by measuring the impedance of the battery at a second time, and the processor determines the first impedance, A battery management system that estimates the life of the battery using the neural network model based on the second impedance, the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery. provides.

Description

Method and apparatus for monitoring battery status in real time}

Embodiments of the present invention relate to a technology for monitoring the state of a battery in real time and a technology for estimating the life of a battery.

Safety is very important for batteries. Be sure to follow the manufacturer's instructions and use the correct charger and charging method. Safety should always be your top priority, as incorrect handling of batteries can result in hazards such as overheating, explosion, or fire.

Estimating the life of a battery can help ensure safe operation of the system, reduce costs, and optimize performance. By estimating the condition and life of the battery, operators can prevent accidents or slowdowns due to insufficient batteries. Batteries are one of the main components of electronic devices, and prediction of battery life allows for efficient management. By estimating the lifespan, you can predict when to replace the battery and save costs by optimizing the replacement cycle. You can reduce costs by avoiding unnecessary battery replacement and ensuring maximum lifespan. Monitoring battery health and lifespan can help you optimize performance.

According to embodiments, a battery management system includes a charging status display unit that displays the charging status of a battery; a temperature sensor that measures the temperature of the battery; an output unit that inputs a constant current to the battery; a measuring unit that measures voltage, current, and impedance of the battery; processor; and a memory, wherein the output unit filters noise of the current transmitted from the battery, removes static electricity applied to the output unit, removes sparks generated by the charge remaining in the capacitor, and performs the measurement. Select a capacitor in response to the voltage measured by the unit, select the frequency of the input current to measure the resistance and reactance of the impedance of the battery, and set the size of the input current in response to the impedance measured by the measurement unit. And, the memory stores the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, the capacity of the battery, the profile for the battery, and a neural network model for predicting the life of the battery, , the measuring unit determines the first impedance by measuring the impedance of the battery at a first time, and determines the second impedance by measuring the impedance of the battery at a second time, and the processor determines the first impedance, The lifespan of the battery can be estimated using the neural network model based on the second impedance, the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery.

The processor estimates the life of the battery based on the first impedance, the second impedance, the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery. If it is decided to proceed, the lifespan of the battery can be estimated using the neural network model.

The neural network model includes an input layer, one or more hidden layers, and an output layer, and the processor determines the resistance and reactance of the first impedance, the number of charging times at the first time point, the temperature at the first time point, and the use of the first time point. A first input vector consisting of time, capacity at a first point in time, resistance and reactance of the second impedance, number of charges at a second point in time, temperature at a second point in time, usage time at a second point in time, and capacity at a second point in time. Determine a second input vector to be constructed, and in a hidden layer, apply a hidden weight to a first hidden state corresponding to the first input vector and apply an input weight to the second input vector to correspond to the second input vector. Calculate a second hidden state, input the second hidden state into a non-linear activation function to output an output vector, and in the output layer, output a probability vector using a softmax function based on the output vector, The lifespan of the battery can be estimated based on the probability vector.

The output unit includes a high-frequency filtering circuit connected to a battery, a surge protection circuit connected to the high-frequency filtering circuit, a spark prevention circuit, a capacitor selection circuit, and a constant current input circuit, and the spark prevention circuit includes the surge protection circuit and the capacitor selection circuit. circuit and the constant current input circuit, the high-frequency filtering circuit filters noise contained in the current transmitted from the battery and transfers it to the spark prevention circuit, and the surge protection circuit removes static electricity inside the output unit. , the spark prevention circuit removes sparks that occur when the battery is connected to the output unit, and removes sparks that occur when the battery is opened with the output unit, and the capacitor selection circuit includes a first capacitor and a second capacitor. 2 capacitors, and when the voltage of the battery measured by the measuring unit is below the threshold voltage, the first capacitor and the second capacitor are connected in parallel, and the voltage of the battery measured by the measuring unit is below the threshold voltage. When the voltage exceeds the first capacitor and the second capacitor are connected in series, the constant current input circuit selects a first frequency to measure the resistance of the impedance of the battery, and the reactance of the impedance of the battery is Select a second frequency to measure, wherein the first frequency is greater than the second frequency, and based on the first frequency or the second frequency, according to a profile including the magnitude of a plurality of currents, starting from the smallest current. The impedance of the battery can be measured by sequentially changing the current.

The neural network model includes an input layer, one or more hidden layers, and an output layer, and each learning data consisting of impedance resistance and reactance, number of charges, temperature, usage time, and capacity is sent to the input layer of the neural network model. It is input and passes through the one or more hidden layers and the output layer to output an output vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer is a function for the output vector and each training data. A loss value is output using a loss function that compares the correct answer vector, and the parameters of the neural network model can be learned in a direction that reduces the loss value.

The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and together with the detailed description describe technical features of the various embodiments.
1 is a diagram showing the configuration of an electronic device according to an embodiment.
Figure 2 is a diagram showing the configuration of a program according to one embodiment.
Figure 3 is a diagram showing the overall configuration of a battery management system according to an embodiment.
Figure 4 is a diagram showing the structure of an output unit according to one embodiment.
Figure 5 is an example diagram for explaining the structure of a neural network model according to an embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may change. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the gist of the various embodiments are not described, and procedures or steps that can be understood at the level of a person with ordinary knowledge in the relevant technical field are not described. did.

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it may further include other elements rather than excluding other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, it may be used in both singular and plural terms.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to illustrate exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

In addition, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the various embodiments. .

1 is a diagram showing the configuration of an electronic device according to an embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or operations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes the main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

Battery 189 may supply power to at least one component of electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and access to multiple terminals (massive machine type communications (mMTC)), or ultra-reliable and low-latency (URLLC). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected. Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band), And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 must perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide services to the connected electronic device 101. In addition, the server 108 may perform a membership registration process, store and manage various information of users who have registered as members, and provide various purchase and payment functions related to the service. Additionally, the server 108 may share execution data of service applications running on each of the plurality of electronic devices 101 in real time so that services can be shared between users. This server 108 may have the same hardware configuration as a typical web server or WAP server. However, in terms of software, it may be implemented through any language such as C, C++, Java, Visual Basic, and Visual C, and may include program modules that perform various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives work performance requests from clients or other servers and derives and provides work results in response. It refers to a computer system and the computer software (server program) installed for it. In addition, in addition to the server program described above, the server 108 includes a series of application programs running on the server 108 and, in some cases, various databases (DBs) built internally or externally, hereinafter " It should be understood as a broad concept including “DB”). Accordingly, the server 108 classifies membership registration information and various information and data about games, stores them in a DB, and manages this DB, which may be implemented inside or outside the server 108. In addition, the server 108 uses a variety of server programs provided depending on the operating system such as DOS, Windows, Linux, UNIX, and Macintosh on general server hardware. Representative examples include Website, IIS (Internet Information Server) used in a Windows environment, and CERN, NCSA, and APPACH used in a Unix environment. Additionally, the server 108 may be linked with an authentication system and payment system for user authentication of the service or payment for purchases related to the service.

The first network 198 and the second network 199 are a connection structure that allows information exchange between each node, such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. It means (Network). The first network 198 and the second network 199 are the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), and 3G. , 4G, LTE, 5G, Wi-Fi, etc., but are not limited to these. The first network 198 and the second network 199 may be closed, such as a LAN or WAN, but are preferably open, such as the Internet. The Internet uses the TCP/IP protocol and several services existing at its upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), and SNMP ( It refers to a worldwide open computer first network (198) and second network (199) structure that provides Simple Network Management Protocol (NFS), Network File Service (NFS), and Network Information Service (NIS).

A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage format that allows for free search (extraction), deletion, editing, addition, etc. of data. Databases are relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and an XML native database such as Excelon, Tamino, and Sekaiju, and has its own function. To achieve this, you can have appropriate fields or elements.

Figure 2 is a diagram showing the configuration of a program according to one embodiment.

Figure 2 is a block diagram 200 illustrating program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable on the operating system 142 for controlling one or more resources of the electronic device 101. It can be included. Operating system 142 may include, for example, AndroidTM, iOSTM, WindowsTM, SymbianTM, TizenTM, or BadaTM. At least some of the programs 140 are preloaded into the electronic device 101, for example, at the time of manufacture, or are stored in an external electronic device (e.g., the electronic device 102 or 104, or a server) when used by a user. It can be downloaded or updated from 108)). All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively operate on other hardware devices of electronic device 101, such as input module 150, audio output module 155, display module 160, and audio module 170. , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 can be used by the application 146. The middleware 144 includes, for example, an application manager 201, a window manager 203, a multimedia manager 205, a resource manager 207, a power manager 209, a database manager 211, and a package manager 213. ), connectivity manager (215), notification manager (217), location manager (219), graphics manager (221), security manager (223), call manager (225), or voice recognition manager (227). You can.

The application manager 201 may, for example, manage the life cycle of the application 146. The window manager 203 may, for example, manage one or more GUI resources used on the screen. For example, the multimedia manager 205 identifies one or more formats required for playing media files, and encodes or decodes the corresponding media file using a codec suitable for the selected format. It can be done. The resource manager 207 may, for example, manage the source code of the application 146 or the memory space of the memory 130. The power manager 209 manages, for example, the capacity, temperature, or power of the battery 189, and may use this information to determine or provide related information necessary for the operation of the electronic device 101. . According to one embodiment, the power manager 209 may interface with a basic input/output system (BIOS) (not shown) of the electronic device 101.

Database manager 211 may create, search, or change a database to be used by application 146, for example. The package manager 213 may, for example, manage the installation or update of applications distributed in the form of package files. The connectivity manager 215 may manage, for example, a wireless connection or direct connection between the electronic device 101 and an external electronic device. For example, the notification manager 217 may provide a function for notifying the user of the occurrence of a designated event (eg, an incoming call, message, or alarm). The location manager 219 may, for example, manage location information of the electronic device 101. The graphics manager 221 may, for example, manage one or more graphic effects to be provided to the user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101. For example, the voice recognition manager 227 transmits the user's voice data to the server 108 and provides a command corresponding to a function to be performed in the electronic device 101 based at least in part on the voice data, Alternatively, text data converted based at least in part on the voice data may be received from the server 108. According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least a portion of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142.

The application 146 includes, for example, home 251, dialer 253, SMS/MMS (255), instant message (IM) 257, browser 259, camera 261, and alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Album (275), Watch (277), Health (279) (such as exercise amount or blood sugar) It may include applications that measure biometric information) or environmental information 281 (e.g., measure atmospheric pressure, humidity, or temperature information). According to one embodiment, the application 146 may further include an information exchange application (not shown) that can support information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to deliver designated information (e.g., calls, messages, or alarms) to an external electronic device, or a device management application configured to manage the external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a specified event (e.g., mail reception) generated in another application (e.g., email application 269) of the electronic device 101 to an external electronic device. You can. Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide it to the user of the electronic device 101.

The device management application, for example, controls the power (e.g., turn-on or turn-off) of an external electronic device or some component thereof (e.g., a display module or camera module of the external electronic device) that communicates with the electronic device 101. ) or functions (such as brightness, resolution, or focus). A device management application may additionally or alternatively support installation, deletion, or update of applications running on external electronic devices.

Figure 3 is a diagram showing the overall configuration of a battery management system according to an embodiment.

According to one embodiment, the battery management system 300 (Battery Management System, BMS) can monitor the status of the battery in real time. The battery management system 300 can determine the status of the battery in real time, such as checking the remaining life of the battery or checking for defective cells in the battery. The battery management system 300 can improve the safety of the system by checking the status of the battery in real time and quickly warning the user when an abnormal condition is detected.

The battery management system 300 provides functions to monitor the safety and performance of the battery and detect abnormal conditions. The battery management system 300 provides reliable information to help prevent problems that may occur during flight and maintain safety.

The battery management system 300 is a system that monitors and controls key parameters such as voltage, current, and temperature of the battery. The battery management system 300 tracks the charging state, discharging state, charging and discharging speed, etc. of the battery and provides information about the battery state to the driver. Additionally, the battery management system 300 can detect dangerous situations such as overcharging, overdischarging, and overheating and issue a warning or take action to protect the battery.

The battery management system 300 monitors voltage and current values in real time through voltage and current sensors. Through these sensors, the battery management system 300 tracks the charging and discharging states of the battery, detects sudden changes in voltage or current, and provides warnings about situations in which problems may occur.

The battery management system 300 detects overheating by monitoring the temperature of the battery through a temperature sensor. Overheating can cause battery performance to deteriorate, or even worse, create a risk of fire or explosion. The battery management system 300 monitors the safe temperature range of the battery through a temperature sensor, and sends a warning to the driver when the temperature exceeds the range to help the driver take action.

The battery management system 300 has a charging status indicator represented by a built-in LED, etc. This indicator indicates the current charging status of the battery and allows the driver to check the remaining battery capacity.

The battery management system 300 provides a function to estimate battery capacity or remaining life. It tracks the remaining energy in the battery and provides information such as expected flight time to help drivers plan their flights.

In this way, the battery management system 300 plays an important role in the safety and performance of the system. Through these systems, drivers can achieve safe operation of the battery by performing regular inspection and maintenance.

To this end, according to one embodiment, the battery management system 300 includes an output unit 310, a measurement unit 320, a processor 330, a memory 340, a temperature sensor 350, and a charging status display unit 360. may include.

The measuring unit 320 measures the current flowing in the battery and the voltage of the battery in response to the current applied through the output unit 310, and can measure the impedance of the battery through these. Generally, as the usage time of the battery increases, chemical by-products accumulate inside the battery, and these by-products increase impedance. The measuring unit 320 may collect data for estimating the life of the battery by measuring the impedance of the battery. The measuring unit 320 may measure the impedance of the battery at different points in time. Multiple time points may have periodicity. For example, the measuring unit 320 determines the first impedance by measuring the impedance of the battery at a first time point, determines the second impedance by measuring the impedance of the battery at a second time point, and determines the impedance at each time point and the impedance. can be matched and stored in the memory 340.

The output unit 310 is configured to apply a specific current to measure the state of the battery. The output unit 310 may apply a current suitable for the characteristics of the battery to measure the battery current, battery voltage, and battery impedance. The output unit 310 can input constant current to the battery step by step.

The output unit 310 may select a capacitor in response to the voltage measured by the measurement unit. The output unit 310 may select the frequency of the input current to measure the resistance and reactance of the impedance of the battery. The measuring unit 320 can selectively measure the resistance and reactance of the battery by adjusting the frequency of the current applied through the output unit 310. The output unit 310 may set the size of the input current in response to the impedance measured by the measurement unit. The measurement unit 320 has a measurement range of up to 800V and can measure voltage up to high-voltage batteries, making it highly versatile compared to other products below 400V. Additionally, compared to other products that only measure resistance, the measuring unit 320 has the advantage of being able to accurately measure the impedance itself by measuring reactance as well. By accurately measuring the entire impedance, the battery's defective state and remaining life can be more accurately estimated.

The output unit 310 can improve accuracy by filtering noise in the current transmitted from the battery. The output unit 310 can remove static electricity applied to the output unit. The output unit 310 can remove sparks generated by charges remaining in the capacitor. The output unit 310 can ensure high safety by including an auxiliary circuit that alleviates a short circuit due to momentary charging that occurs in the coupling capacitor when a high voltage is applied to the circuit.

The temperature sensor 350 monitors the temperature of the battery and detects overheating. Temperature sensor 350 monitors the battery's safe temperature range and alerts the driver if the temperature exceeds the range so that he or she can take action.

The processor 330 can monitor the impedance of the battery in real time. The processor 330 can measure the impedance of the battery at regular time periods and analyze changes in impedance. The processor 330 can estimate the lifespan of the battery through the impedance analysis results. The processor 330 can warn or recommend replacement when the battery life is at a critical level. Additionally, the processor 330 can efficiently manage battery energy by saving unnecessary energy in real time.

Memory 340 may store impedance measured over time. The memory 340 can store basic resources, basic information or profile of the battery, capacity of the battery, usage time of the battery, number of charging times of the battery, temperature measured by the temperature sensor, and a neural network model for estimating the life of the battery. there is.

The charging status display unit 360 can express the charging status of the battery using a built-in LED. The charging status display unit 360 indicates the current charging status of the battery so that the driver can check the remaining battery capacity.

Below, the process of estimating the remaining life of the battery is described.

The processor 330 may detect an impedance higher than the initial impedance and determine that the life of the battery has been reduced. The processor 330 sets the initial impedance as a reference. This initial impedance represents the optimal state of the battery and is at its maximum lifetime. The initial impedance is accurately measured and stored before use of the battery and used for comparison.

The processor 330 compares the real-time impedance value with the initial impedance value and analyzes the difference. If the impedance has increased significantly from the initial impedance, this may indicate that the internal resistance of the battery has increased, possibly reducing its lifespan. An increase in impedance may occur due to changes in chemicals or accumulated corrosion, which are internal components of the battery. The processor 330 may estimate the expected lifespan of the battery by analyzing the pattern of lifespan decrease as impedance increases. Through this, the processor 330 can provide the driver with an advance warning about a battery whose lifespan is decreasing.

If an increase in impedance is detected, the processor 330 can send a warning to the driver to inform him or her that the life of the battery has decreased. Alerts can be delivered visually, audio or through notification messages, even during flight. Additionally, the processor 330 may provide battery replacement recommendations and guide the driver to replace a battery whose lifespan has decreased.

In this way, the battery management system 300 can detect changes in the lifespan of the battery through impedance measurement and comparison, and provide the corresponding information to the driver to help monitor the state of the battery and perform maintenance.

The method of using a neural network model to predict lifespan decline can be explained as follows.

The battery management system 300 may collect characteristics and lifespan information of various batteries to predict lifespan reduction. This data can include a variety of input variables such as initial impedance, battery capacity, driving time, number of charges, temperature, etc. Additionally, label data recording the actual lifespan of each battery may be included.

The input variables of the neural network model consist of various variables that represent the characteristics of the battery. These variables represent the status and usage information of the battery and can be, for example, initial impedance, battery capacity, driving time, number of charges, temperature, current impedance, etc.

The collected data goes through a preprocessing process and is processed into a form suitable for model learning. In this process, data normalization, outlier processing, missing value replacement, etc. can be performed. The data is divided into training data, validation data, and test data and is used to evaluate the model.

The neural network model for predicting lifespan reduction uses input variables and the lifespan information of the relevant battery to learn and predict the model. The structure of the model can consider various architectures, such as multilayer perceptron (MLP), recurrent neural network (RNN), and modified autoregressive models (LSTM, GRU). In this specification, the neural network model may be designed as an RNN model.

Neural network models can be learned through training data. In this process, the model learns the relationship between input variables and lifespan information to predict lifespan decline. The performance of the learned model on validation data is evaluated, and optimal model parameters are determined.

Each learning data consisting of impedance resistance and reactance, number of charges, temperature, usage time, and capacity is input to the input layer of the neural network model and passes through one or more hidden layers and output layers to output an output vector, and the output vector is It is input to the loss function layer connected to the output layer, and the loss function layer outputs the loss value using a loss function that compares the output vector with the correct answer vector for each training data, and the parameters of the neural network model have a small loss value. It can be learned in a losing direction.

[Equation 1]

The loss function may follow Equation 1 above.

In Equation 1, n is the number of learning data for each class, y and j are identifiers representing the classes, C is a constant value, M is the number of classes, x_y is the probability that the learning data belongs to class y, and x_j is the learning data. is the probability value of belonging to class j, L is the loss value,

Since Equation 1 reflects the number of learning data for each class, a class with a small number of learning data may have a small impact on learning, and a class with a large number of learning data may have a large impact on learning.

Processor 330 may first determine whether to perform lifetime reduction prediction. Before estimating the life of the battery, the processor 330 may determine whether to estimate the life of the battery. The processor 330 may determine whether to proceed with estimating the life of the battery based on the first impedance, the second impedance, the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery. . Here, the first impedance refers to the impedance measured at the first time point, and the second impedance refers to the impedance measured at the second time point. If it is decided to proceed, the processor 330 can estimate the lifespan of the battery using a neural network model. The processor 330 can save resources of the battery management system 300 by making predictions only when it is determined that battery life will be affected, rather than running the model at every time.

The processor 330 may obtain a comparison value using Equation 2, and compare the comparison value with a reference value to first determine whether to perform a lifespan reduction prediction. The processor 330 may perform a lifespan reduction prediction when the comparison value is greater than the reference value. The reference value can be set in advance through learning depending on the type of battery.

[Equation 2]

The processor 330 may determine the state vector based on information about the first impedance, the second impedance, the number of times the battery has been charged, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery. A state vector is data in the form of a vector consisting of data related to the state of the battery at a specific point in time. For example, the state vectors S1 and S2 may be 7-dimensional vectors of impedance R value, X value, number of charges, temperature, usage time, and capacity at the first and second time points, respectively.

n is the number of dimensions of the state vector, P1i is the value of the ith dimension of the first state vector, P2i is the value of the ith dimension of the second state vector, ai is the weight corresponding to the ith dimension set by learning, b refers to the weight applied to the summary set by learning. The min function is a function that determines the smaller value among the two values in parentheses.

The learned model can perform lifespan reduction predictions for new batteries. To do this, if the input variables of a new battery are provided to the model, the model can predict the decrease in life of that battery. The output of the model may be the expected lifespan or the relative degradation of the lifespan.

A neural network model may include an input layer, one or more hidden layers, and an output layer. Neural network models have powerful modeling capabilities for sequential inputs such as time series data.

The processor 330 can estimate the life of the battery using a neural network model based on the first impedance, the second impedance, the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery. there is. Here, the first impedance refers to the impedance measured at the first time point, and the second impedance refers to the impedance measured at the second time point. The first point in time is a point in time preceding the second point in time. In the following, only two viewpoints are assumed for convenience of explanation, but this is only an example, and data from three or more viewpoints may be involved. The time between each point in time may be constant, or it may be inconsistent if measured according to a specific event.

The input layer can receive as input a state vector constructed based on various variables related to the seven-dimensional battery state such as impedance R value, X value, number of charges, temperature, usage time, and capacity at a specific point in time. The processor 330 is a first input vector consisting of the resistance and reactance of the first impedance, the number of charging times at the first time, the temperature at the first time, the usage time at the first time, and the capacity at the first time. A second input vector consisting of resistance and reactance, the number of charges at the second point in time, the temperature at the second point in time, the usage time at the second point in time, and the capacity at the second point in time can be determined.

Hidden layers may contain recursive layers. The hidden layer remembers the input values of the previous time step and processes them together with the input of the current time step. The hidden layer internally uses weights and activation functions to process input data and produce output. The hidden layer remembers the input values from the previous time step, considers them at the current time step, and reflects them in the prediction. In the hidden layer, processor 330 calculates a second hidden state corresponding to the second input vector by applying a hidden weight to the first hidden state corresponding to the first input vector and applying the input weight to the second input vector. , the second hidden state can be input to the non-linear activation function to output an output vector.

The output layer produces the final output of the neural network model. This output contains desired output data, such as predicted battery life. The output layer adjusts the model's output value through an activation function to fit the prediction range. In the output layer, the processor 330 may output a probability vector using a softmax function based on the output vector. The processor 330 may estimate the lifespan of the battery based on the probability vector.

For example, if multi-class classification is required, the softmax function can be used in the output layer. For example, output layer has 10% lifetime reduction, 20% lifetime reduction ?? Ten classes of 90% lifespan reduction and 100% lifespan reduction can be classified, and the final vector can represent the probability for each class. For example, if the final vector is (0.001,0.987, ??,0.003), the processor 330 may estimate that the lifespan has been reduced by 20% compared to the initial state.

In this way, the battery management system 300 can improve the performance and efficiency of the battery by learning and predicting the relationship between the increase in impedance of the battery and the decrease in lifespan using a neural network model.

Throughout this specification, neural network, neural network, and network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may generally be referred to as “nodes.” These “nodes” may also be referred to as “neurons.” A neural network is composed of at least two or more nodes. Nodes (or neurons) that make up neural networks may be interconnected by one or more “links.”

Within a neural network, two or more nodes connected through a link can relatively form a relationship as an input node and an output node. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, nodes connecting the input node and the output node may have weights. Weights may be variable and may be varied by a user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if there are two neural networks with the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

Figure 4 is a diagram showing the structure of an output unit according to one embodiment.

Referring to FIG. 4, the output unit 310 includes a high-frequency filtering circuit 311 connected to the battery 401, a surge protection circuit 315 connected to the high-frequency filtering circuit 311, a spark prevention circuit 317, and a capacitor. It may include a selection circuit 313 and a constant current input circuit 319. The spark prevention circuit 317 may be connected to the surge protection circuit 315, the capacitor selection circuit 313, and the constant current input circuit 319.

The high-frequency filtering circuit 311 may filter noise contained in the voltage transmitted from the battery 401 and transmit it to the spark prevention circuit 317. In Figure 4, BD1 is a bead connected to the positive terminal of the battery, and BD2 is a bead connected to the negative terminal of the battery. BD1 and BD2 filter high-frequency noise.

The surge protection circuit 315 (Transient Voltage Suppressors, TVS) can remove static electricity inside the output unit 310. For example, static electricity of several thousand to tens of thousands of volts may be generated inside the output unit 310, and the surge protection circuit 315 can send this high voltage to the ground.

The spark prevention circuit 317 may remove sparks that occur when the battery is connected to the output unit and remove sparks that occur when the battery is opened to the output unit. The moment the battery 401 falls from the measuring unit 310, sparks may occur in the measuring unit 310, and the spark prevention circuit 317 can prevent sparks by transferring charge to the ground through R1 and R2. For example, R1 may be 220k and R2 may be 10M. At this time, the spark prevention circuit 317 may connect two switches to terminal 7 and terminal 2, respectively. The moment the battery 401 contacts the measuring part 310, a spark may occur in the measuring part 310, and the spark prevention circuit 317 prevents sparks by transferring charge to the ground through R3, R4, and D4. You can. At this time, the spark prevention circuit 317 may connect two switches to terminals 4 and 5, respectively.

The capacitor selection circuit 313 may include a first capacitor (C1) and a second capacitor (C2). The capacitor selection circuit 313 may connect the first capacitor and the second capacitor in parallel when the voltage of the battery 401 measured by the measuring unit 320 is below the threshold voltage. The capacitor selection circuit 313 may connect the first capacitor and the second capacitor in series when the voltage of the battery 401 measured by the measurement unit 320 exceeds the threshold voltage.

For example, the threshold voltage may be 200V. To measure the impedance of the low-voltage battery, the capacitor selection circuit 313 may connect the first capacitor and the second capacitor in parallel. The capacitor selection circuit 313 can connect two switches to terminals 5 and 4, respectively. Here, low voltage may mean DC 0V - 200V. To measure the impedance of the high-voltage battery, the capacitor selection circuit 313 may connect a first capacitor and a second capacitor in series. The capacitor selection circuit 313 may connect two switches to terminal 7 and terminal 2, respectively. Here, high voltage may mean DC 201V - 800V. In this way, the impedance of both low-voltage and high-voltage batteries can be measured through selective combination of capacitors.

The constant current input circuit 319 may select a first frequency to measure the resistance of the impedance of the battery 401. The constant current input circuit 319 may select a second frequency to measure the reactance of the impedance of the battery. Here, the first frequency is greater than the second frequency. For example, the first frequency may be 1 kHz and the second frequency may be 100 Hz. Assuming that the battery is a large capacitor, the capacitor value may not be measured accurately if a 1 kHz source is applied, and the capacitor value may be measured more accurately by applying a 100 Hz source. Here, 100Hz is the frequency with the highest measurement accuracy of the capacitor of a commonly used battery, but is not limited to this and other low frequencies may be applied.

Based on the first frequency or the second frequency, the constant current input circuit 319 can sequentially change the current starting from the smallest current according to a profile including the magnitude of a plurality of currents, and the measuring unit 310 responds accordingly. You can measure the impedance of the battery. The constant current input circuit 319 may include U1, U2, and U3. The constant current input circuit 319 can use profiles with sizes of 100 mA, 10 mA, 1 mA, and 0.1 mA. If the resistance is high but the current is also high, the voltage of a specific element within the circuit may reach a saturation voltage, so the constant current input circuit 319 can measure impedance while applying current in a direction starting from the lowest current and increasing. For example, the constant current input circuit 319 can apply a current of 0.1 mA and receive feedback on the size of the resistance from the measuring unit 320. If the feedback result is within the allowable range, the constant current input circuit 319 can change the size of the current to 1 mA and repeat the same process. If the result of the feedback exceeds the allowable range, the constant current input circuit 319 can maintain a current of 0.1 mA. As the above process is repeated, the optimal current size can be selected.

Figure 5 is an example diagram for explaining the structure of a neural network model according to an embodiment.

According to one embodiment, the neural network model used in the battery management system 300 may include a recurrent neural network model. The neural network model is a sequence model that processes input in sequence units.

A neural network model may include an input layer, one or more hidden layers, and an output layer. Hidden layers may contain recursive layers. The hidden layer has the characteristic of sending the result value from the activation function at the node to the output direction and then sending it back to the input of the next calculation of the node of the hidden layer. The node responsible for sending out results through the activation function in the hidden layer may be referred to as a cell. Cells can simultaneously serve as a type of memory that remembers previous values.

The value sent by the cell to the output direction or to the cell at the next point in time, t+1, may be referred to as a hidden state. The cell at time t can use the hidden state sent by the cell at time t-1 as an input for calculating the hidden state at time t. Referring to model 520, first viewpoint, second viewpoint, third viewpoint ?? Output vectors (521, 523, 525, 527) at time t are displayed. The output vector y1 521 may be determined based on the starting hidden state and the input vector x1 at the first time point. The output vector y2 523 may be determined based on the hidden state at the first time point and the input vector x2 at the second time point. In the same way, the same process is repeated until the tth time point, and the output vector yt (527) at the tth time point can be output.

For example, the battery management system 300 may estimate the lifespan of the battery for each state vector at 10 units of time. In this case, state vectors from the first to the tenth time points are input to 10 cells, and among the output vectors output from each cell, only the last output vector is delivered to the output layer and the remaining output vectors are discarded. For example, a neural network model may be a model with a many-to-one structure that outputs one output for multiple inputs. The final output vector is classified into one of 10 classes through the softmax function. Each class represents the percentage of reduced life compared to the initial life of the battery, and the remaining life is estimated based on the reduced life corresponding to the class with the highest probability.

The model 510 is an example that embodies two specific cells of the model 520, and the input vector has a dimension of 4, the hidden state has a dimension of 2, and the output vector of the output layer has a dimension of 2. In other words, when interpreted in units of neurons, the number of neurons in the input layer is 4, the number of neurons in the hidden layer is 2, and the number of neurons in the output layer is 2. At time t 512, the two-dimensional output vector at time t is calculated based on the four-dimensional input vector at time t and the two-dimensional hidden state at time t-1.

Model 530 is a simplified version of model 510. If the hidden state value at the current time t is defined as ht, the cells of the hidden layer have a total of two weights to calculate ht. One is the weight W2 for the input layer, and the other is the weight Wh for ht-1, which is the hidden state of the previous time t-1.

[Equation 3]

[Equation 4]

The hidden state at the current time t can be calculated by Equation 3. The activation function used here may be a hyperbolic tangent function (tanh). The output vector at the current time t can be calculated by Equation 4. Here, function f is a non-linear activation function. b is bias.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes magneto-optical media and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (5)

A charging status display unit that displays the charging status of the battery;
a temperature sensor that measures the temperature of the battery;
an output unit that inputs a constant current to the battery;
a measuring unit that measures voltage, current, and impedance of the battery;
processor; and
contains memory,
The output unit,
Filtering out noise from the current delivered from the battery,
Remove static electricity applied to the output unit,
Eliminate sparks caused by charges remaining in the capacitor of the battery,
Selecting a capacitor in response to the voltage measured by the measuring unit,
Select the frequency of the input current to measure the resistance and reactance of the impedance of the battery,
Set the size of the input current in response to the impedance measured by the measuring unit,
The memory stores a neural network model for predicting the number of charges of the battery, the temperature measured by the temperature sensor, the usage time of the battery, the capacity of the battery, the profile for the battery, and the life of the battery,
The measuring unit,
Determine a first impedance by measuring the impedance of the battery at a first time,
Determine a second impedance by measuring the impedance of the battery at a second time point,
The processor,
The lifespan of the battery is determined using the neural network model based on the first impedance, the second impedance, the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery. estimate,
The processor,
Life of the battery by comparing Equation 2 and a reference value based on the first impedance, the second impedance, the number of charging times of the battery, the temperature measured by the temperature sensor, the usage time of the battery, and the capacity of the battery. Decide whether to proceed with the estimation,
If it is decided to proceed, estimate the lifespan of the battery using the neural network model,
The neural network model includes an input layer, one or more hidden layers, and an output layer,
Each learning data consisting of impedance resistance and reactance, number of charges, temperature, usage time, and capacity is input to the input layer of the neural network model, passes through the one or more hidden layers and the output layer, and outputs an output vector, The output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using the loss function of Equation 1, which compares the output vector with the correct answer vector for each training data, , the parameters of the neural network model are learned in the direction of decreasing the loss value,
[Equation 1]

In Equation 1, n is the number of learning data for each class, y and j are identifiers representing the classes, C is a constant value, M is the number of classes, x_y is the probability that the learning data belongs to class y, and x_j is the learning data. is the probability value of belonging to class j, L is the loss value,
[Equation 2]

In Equation 2, n is the number of dimensions of the state vector, P1i is the value of the ith dimension of the first state vector, P2i is the value of the ith dimension of the second state vector, and ai is the ith dimension set by learning. The corresponding weight, b, is the weight applied to the summation set by learning, and the min function represents a function that determines the smaller value among the two values in parentheses.
Battery management system. delete According to paragraph 1,
The neural network model includes an input layer, one or more hidden layers, and an output layer,
The processor,
A first input vector consisting of the resistance and reactance of the first impedance, the number of charges at the first time, the temperature at the first time, the usage time at the first time, and the capacity at the first time, and the resistance and reactance of the second impedance , determine a second input vector consisting of the number of charges at the second time point, the temperature at the second time point, the usage time at the second time point, and the capacity at the second time point,
In the hidden layer, calculate a second hidden state corresponding to the second input vector by applying a hidden weight to the first hidden state corresponding to the first input vector and applying the input weight to the second input vector, and Input the second hidden state into a nonlinear activation function to output an output vector,
In the output layer, a probability vector is output using the softmax function based on the output vector,
Estimating the life of the battery based on the probability vector,
Battery management system. According to paragraph 1,
The output unit includes a high-frequency filtering circuit connected to a battery, a surge protection circuit connected to the high-frequency filtering circuit, a spark prevention circuit, a capacitor selection circuit, and a constant current input circuit, and the spark prevention circuit includes the surge protection circuit and the capacitor selection circuit. connected to the circuit and the constant current input circuit,
The high-frequency filtering circuit filters noise contained in the current transmitted from the battery and transmits it to the spark prevention circuit,
The surge protection circuit removes static electricity inside the output unit,
The spark prevention circuit removes sparks that occur when the battery is connected to the output portion and removes sparks that occur when the battery is opened with the output portion,
The capacitor selection circuit includes a first capacitor and a second capacitor, connects the first capacitor and the second capacitor in parallel when the voltage of the battery measured by the measuring unit is below the threshold voltage, and connects the measuring unit to the first capacitor. When the voltage of the battery measured by exceeds the threshold voltage, connect the first capacitor and the second capacitor in series,
The constant current input circuit is,
A first frequency is selected to measure the resistance of the impedance of the battery, and a second frequency is selected to measure the reactance of the impedance of the battery, wherein the first frequency is greater than the second frequency,
Based on the first frequency or the second frequency, changing the current sequentially from the smallest current according to a profile including the magnitude of a plurality of currents and measuring the impedance of the battery,
Battery management system. delete

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