WO2021201387A1 - Procédé et dispositif d'estimation de capacité de batterie basée sur un réseau neuronal - Google Patents

Procédé et dispositif d'estimation de capacité de batterie basée sur un réseau neuronal Download PDF

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WO2021201387A1
WO2021201387A1 PCT/KR2021/000136 KR2021000136W WO2021201387A1 WO 2021201387 A1 WO2021201387 A1 WO 2021201387A1 KR 2021000136 W KR2021000136 W KR 2021000136W WO 2021201387 A1 WO2021201387 A1 WO 2021201387A1
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charging
neural network
battery
learning
voltage
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Korean (ko)
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정승호
정승현
김상우
박민준
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(주)아르고스다인
포항공과대학교 산학협력단
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Publication of WO2021201387A1 publication Critical patent/WO2021201387A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present invention relates to a method for estimating battery capacity, and more particularly, to a method and apparatus for estimating battery capacity based on a neural network.
  • lithium secondary batteries have almost no memory effect compared to nickel-based secondary batteries, so charging and discharging are free, Due to its advantages such as a very low self-discharge rate and high energy density, it is receiving a lot of attention.
  • lithium ions start to accumulate on the surface of the positive electrode or negative electrode, not between the layered structures. Due to this, the capacity of the battery is gradually reduced, and in the worst case, the separator that prevents the short circuit of the battery is pierced, which may also cause a problem in the stability of the battery.
  • the learning-based capacity estimation algorithm using machine learning includes a “CV (Constant Voltage) charging-based battery capacity estimation algorithm” and “CC (Constant Current) charging-based battery capacity estimation algorithm”.
  • CV Constant Voltage
  • CC Constant Current
  • the CV charging-based battery capacity estimation algorithm requires full charging to measure the characteristics displayed in the charging period.
  • the method cannot be used because the charging section characteristic cannot be constantly measured due to the cell balancing function.
  • the CC charging-based battery capacity estimation algorithm estimates the battery capacity by using the characteristics that exist in the low SOC (State of Charge) section, in order to use the algorithm, the battery is used up to the low SOC section just before charging. need to discharge. As such, since the CC charging-based battery capacity estimation algorithm discharges the battery to a low SOC section just before charging, the lifespan of the entire battery is reduced, and there is a problem that it takes a long time.
  • SOC State of Charge
  • Patent Document 1 Domestic Patent Publication No. 10-2018-0121317
  • Patent Document 2 Domestic Registered Patent No. 10-2032229
  • the present specification has been devised to solve the above problems, and in CC charging, a method and apparatus for estimating battery capacity based on a neural network capable of estimating the capacity of a battery only with data from an arbitrary charging start time to the charging end Its purpose is to provide
  • Another object of the present invention is to provide a method and apparatus for estimating battery capacity based on a neural network capable of estimating battery capacity without having to discharge to a low SOC section just before charging.
  • the neural network learning method is a method for learning a neural network in a computing device for constant current charging-based battery capacity estimation, any collecting the remaining capacity of the battery from the start of charging of the to the time when the maximum voltage is reached during charging; and learning the neural network by inputting the collected residual capacity into the neural network.
  • the neural network is implemented through a multi-layer perceptron or a fully connected layer.
  • the neural network learning method is a method for learning a neural network in a computing device for estimating battery capacity based on constant current charging, during charging from an arbitrary charging start time. collecting the charging voltage up to the point in time when the maximum voltage is reached; and learning the neural network by inputting a charging time, a collected charging voltage, and a battery remaining capacity at the arbitrary charging start time to the neural network.
  • the voltage curve of the charging section is divided by N (N is a natural number) at equal intervals to collect the charging voltage for each section.
  • the method further comprises the step of assigning a preset weight for each type of battery to the neural network.
  • the neural network is implemented through a multi-layer perceptron or a fully connected layer.
  • the neural network learning apparatus includes a memory for storing one or more instructions: and by executing the stored one or more instructions, from any charging start time to the maximum during charging. It includes one or more processors that collect the charging voltage until the voltage is reached, and configure a neural network for estimating the capacity of the battery by receiving the charging time, the collected charging voltage, and the remaining capacity of the battery at any charging start time. .
  • the one or more processors divide the voltage curve of the charging section into N equal parts (N is a natural number) at equal intervals, and collect the charging voltage for each section.
  • the one or more processors assign a preset weight to the neural network for each battery type.
  • the neural network is implemented through a multi-layer perceptron or a fully connected layer.
  • the neural network-based battery capacity estimation for estimating the battery capacity by inputting and learning the remaining capacity of the battery collected from the start of any charging to the time when the maximum voltage is reached during charging into the neural network.
  • the data server 130 replaces the remaining capacity when the maximum voltage is reached during charging and uses the charging time, the charging voltage, and the remaining capacity at the charging start time as a feature point for learning.
  • FIG. 1 is a block diagram showing a schematic configuration of a battery capacity estimation system according to an embodiment of the present invention
  • FIG. 2 is a diagram showing a voltage curve and a dV/dQ curve
  • FIG. 3 is a block diagram showing an example of hardware capable of realizing the function of the battery capacity estimation device according to the embodiment of the present invention
  • FIG. 4 is a block diagram showing an example of a function of the battery capacity estimation device according to the embodiment of the present invention.
  • FIG. 5 is a flowchart illustrating a method for estimating battery capacity based on a neural network according to a first embodiment of the present invention
  • FIG. 6 is a flowchart illustrating a method for estimating battery capacity based on a neural network according to a second embodiment of the present invention
  • FIG. 8 is a diagram for explaining the structure and implementation of a multi-perceptron.
  • first, second, etc. used herein may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.
  • SOC is an abbreviation for State of Charge, which indicates the state of charge of the battery, and can be expressed by Equation 1 below.
  • Q remaining represents the remaining capacity of the battery
  • Q max represents the capacity of the battery in a fully charged state
  • SOC init and SOC cctocv indicate a charging state at a specific point in the charging process. That is, SOC init indicates the remaining capacity at the start of charging, and SOC cctocv indicates the remaining capacity at the time when the maximum voltage is reached during charging.
  • FIG. 1 is a block diagram showing a schematic configuration of a battery capacity estimation system according to an embodiment of the present invention.
  • the battery capacity estimation system may include a battery charging device 110 , an AP 120 , and a data server 130 .
  • the battery charging device 110 and the data server 130 may be implemented as one device.
  • the battery charging device 110 is a device for charging a battery using a constant current (hereinafter, referred to as 'CC').
  • the battery charging device 110 does not separately discharge the battery, and charges the battery regardless of the current SOC of the battery.
  • the battery charging device 110 collects data such as the charging voltage (V cc ) and the remaining capacity of the battery from an arbitrary charging start time to a time when the maximum voltage is reached during charging, and collects the collected data.
  • the data is transmitted to the battery capacity estimating device, that is, the data server 130 via the AP 120 .
  • the battery charging device 110 may divide the voltage curve of the charging section into N equal intervals (here, N is a natural number) and collect V cc for each section.
  • the data server 130 estimates the capacity of the battery by analyzing the remaining capacity of the battery collected by the battery charging device 110 through artificial intelligence (AI).
  • AI artificial intelligence
  • Equation 2 the capacity of the battery may be expressed by Equation 2 below.
  • I cc represents the charging current and t cc represents the charging time.
  • the data server 130 estimates the capacity of the battery by comparing I cc ⁇ t cc and SOC cctocv - SOC init .
  • the data server 130 may be implemented as a neural network composed of one or more computers, and may estimate the capacity of the battery by inputting the collected data into the neural network.
  • the data server 130 inputs and learns the remaining capacity of the battery collected from the start of any charging to the time when the maximum voltage is reached during charging into the neural network to learn the battery until the low SOC section just before charging.
  • the capacity of the battery can be estimated without the need to discharge the battery.
  • FIG. 2(a) shows a voltage curve
  • FIG. 2(b) shows a dV/dQ curve.
  • FIG. 2 it can be seen that nonlinear features 210 according to SOC are observed in the voltage curve and the dV/dQ curve.
  • Reference numeral 220 of the dV/dQ curve denotes distortion generated during the filtering process, and is not a nonlinear feature point.
  • the data server 130 can replace SOC cctocv and use t cc , V cc , and SOC init as key points for learning. That is, the data server 130 may estimate the capacity of the battery by inputting t cc , V cc , and SOC init to the neural network and learning. In addition, the data server 130, when the battery charging device 110 divides the voltage curve of the charging section into N equal intervals, for each section collected by the battery charging device 110 together with t cc and SOC init . By inputting V cc to the neural network and learning it, the capacity of the battery can be estimated.
  • the data server 130 may estimate the capacity of the battery through machine learning using a multi-layer perceptron algorithm, which is a kind of artificial neural network.
  • An artificial neural network is an algorithm that models the regularity between input data and output data through learning using a given training dataset as an input.
  • the artificial neural network that has completed the learning process may predict the output data with respect to the untrained input data through regularity modeled through the training data set.
  • the algorithm proposed in this embodiment is an algorithm for estimating the capacity of a battery with input data measured during charging in a real situation by using an artificial neural network trained with a learning data set constructed through a prior experiment.
  • the data server 130 can find regularity through learning only with information present in V cc , t cc , and SOC int , and estimate the battery capacity (C max ).
  • the data server 130 divides the charging period into N (eg, 100) equal parts to input V cc to find regularity by sufficiently reflecting information present in the voltage curve.
  • N eg, 100
  • V cc is less or more than 100 equal parts.
  • the data server 130 replaces SOC cctocv and uses t cc , V cc , and SOC init as key points for learning. Therefore, it is possible to exclude the change of the SOC cctocv point.
  • the data server 130 considers changes in characteristics such as the shape of the charging voltage or the charging voltage curve according to the series/parallel connection type of the battery pack or the internal chemical composition of individual batteries, and sets a weight preset for each type of battery in the neural network. may be given.
  • 3 is a block diagram showing an example of hardware capable of realizing the function of the battery capacity estimation device according to the embodiment of the present invention.
  • the function of the battery capacity estimating device 130 can be realized using, for example, the hardware resources shown in FIG. 3 . That is, the function of the battery capacity estimation device 130 is realized by controlling the hardware shown in FIG. 3 using a computer program.
  • this hardware mainly includes a CPU 302 , a Read Only Memory (ROM) 304 , a RAM 306 , a host bus 308 , and a bridge 310 .
  • the hardware includes an external bus 312 , an interface 314 , an input unit 316 , an output unit 318 , a storage unit 320 , a drive 322 , a connection port 324 , and a communication unit 326 .
  • the CPU 302 functions, for example, as an arithmetic processing unit or a control unit, based on various programs recorded in the ROM 304 , the RAM 306 , the storage unit 320 , or the removable recording medium 328 . Controls all or part of the operation of each component.
  • the ROM 304 is an example of a storage device that stores a program read by the CPU 302, data used for calculation, and the like.
  • the RAM 306 for example, a program read by the CPU 302, various parameters that change when the program is executed, and the like are temporarily or permanently stored.
  • a host bus 308 capable of high-speed data transfer.
  • the host bus 308 is connected to an external bus 312 having a relatively low data transfer rate, for example, via a bridge 310 .
  • the input unit 316 for example, a mouse, a keyboard, a touch panel, a touch pad, a button, a switch, a lever, and the like are used.
  • a remote controller capable of transmitting a control signal using infrared or other radio waves may be used.
  • a display device such as a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display panel (PDP), or an electro-luminescence display (ELD) may be used.
  • an audio output device such as a speaker or headphones, or a printer may be used.
  • the storage unit 320 is a device for storing various data.
  • a magnetic storage device such as an HDD is used.
  • a semiconductor storage device such as an SSD (Solid State Drive) or a RAM disk, an optical storage device, a magneto-optical storage device, or the like may be used.
  • the drive 322 is a device that reads information recorded on the removable recording medium 328 which is a removable recording medium or writes information into the removable recording medium 328 .
  • the removable recording medium 328 for example, a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory is used. Also, in the removable recording medium 328 , a program for regulating the operation of the battery capacity estimating apparatus 130 may be stored.
  • Connection port 324 is, for example, a USB (Universal Serial Bus) port, IEEE 1394 port, SCSI (Small Computer System Interface), RS-232C port, or an optical audio terminal, such as an external connection device 330 for connecting it's a port
  • a printer or the like is used as the externally connected device 330.
  • the communication unit 326 is a communication device for connecting to the network 332 .
  • a communication circuit for wired or wireless LAN for example, a communication circuit for WUSB (Wireless USB), a communication circuit for a cellular phone network, or the like can be used.
  • the network 332 is, for example, a network connected by wire or wireless.
  • the hardware of the battery capacity estimation apparatus 130 has been described above.
  • the above-mentioned hardware is an example, and a deformation
  • FIG. 4 is a block diagram showing an example of a function of the battery capacity estimation device according to the embodiment of the present invention.
  • the battery capacity estimation device 130 may include a storage unit 232 , a battery data collection unit 234 , and a battery capacity estimation unit 236 .
  • storage part 232 is implement
  • the function of the battery data collection unit 234 can be realized by using the above-described communication unit 326 or the like.
  • the function of the battery capacity estimation unit 236 can be realized using the above-described CPU 302 or the like.
  • the storage unit 232 stores data including the charging voltage and the remaining capacity of the battery.
  • the battery data collection unit 234 collects data such as the charge voltage and the remaining capacity of the battery from an arbitrary charging start time to the time when the maximum voltage is reached during charging while charging the battery and stores the data in the storage unit 232 . .
  • the battery data collection unit 234 may divide the voltage curve of the charging section into N equal parts (N is a natural number) at equal intervals to collect V cc for each section.
  • the battery capacity estimating unit 236 estimates the battery capacity by inputting and learning the remaining capacity of the battery collected from an arbitrary charging start time to a time when the maximum voltage is reached during charging into the neural network.
  • the battery capacity estimator 236 may replace SOC cctocv and use t cc , V cc , and SOC init as key points for learning. That is, the battery capacity estimator 236 may estimate the capacity of the battery by inputting t cc , V cc , and SOC init to the neural network and learning. At this time, the battery capacity estimating unit 236 is collected by the battery data collection unit 234 together with t cc and SOC init when the battery data collection unit 234 divides the voltage curve of the charging section into N equal intervals. The capacity of the battery can be estimated by inputting V cc for each section to the neural network and learning it.
  • the battery capacity estimator 236 may give the neural network a preset weight for each battery type.
  • FIG. 5 is a flowchart illustrating a method for estimating battery capacity based on a neural network according to the first embodiment of the present invention.
  • the battery capacity estimating apparatus 130 collects the remaining capacity of the battery from an arbitrary charging start time to a time when the maximum voltage is reached during charging ( S510 ).
  • the battery capacity estimating apparatus 130 estimates the battery capacity by inputting the collected residual capacity of the battery from the start of any charging to the time when the maximum voltage is reached during charging into the neural network and learning ( S520 ). In this case, the battery capacity estimating apparatus 130 may give the neural network a preset weight for each battery type.
  • FIG. 6 is a flowchart illustrating a method for estimating battery capacity based on a neural network according to a second embodiment of the present invention.
  • the battery capacity estimating device 130 collects a charging voltage (V cc ) from an arbitrary charging start time to a time point reaching the maximum voltage during charging ( S610 ).
  • the battery capacity estimating apparatus 130 may divide the voltage curve of the charging section into N equal parts (N is a natural number) at equal intervals to collect V cc for each section.
  • the battery capacity estimating apparatus 130 estimates the capacity of the battery by inputting t cc , V cc , and SOC init to the neural network to learn ( S620 ). At this time, when the voltage curve of the charging section is divided into N equal intervals, the battery capacity estimating device 130 inputs V cc for each section collected together with t cc and SOC init to the neural network and learns, thereby the capacity of the battery can be estimated. Also, the battery capacity estimating apparatus 130 may give the neural network a preset weight for each battery type.
  • embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.
  • the method according to embodiments of the present invention may include one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), and Programmable Logic Devices (PLDs). , FPGAs (Field Programmable Gate Arrays), processors, controllers, microcontrollers and microprocessors, and the like.
  • ASICs Application Specific Integrated Circuits
  • DSPs Digital Signal Processors
  • DSPDs Digital Signal Processing Devices
  • PLDs Programmable Logic Devices
  • FPGAs Field Programmable Gate Arrays
  • processors controllers
  • the method according to the embodiments of the present invention may be implemented in the form of a module, procedure, or function that performs the functions or operations described above.
  • the software code may be stored in the memory unit and driven by the processor.
  • the memory unit may be located inside or outside the processor, and may transmit and receive data to and from the processor by various known means.
  • the artificial neural network may be configured by stacking several layers, such as a fully connected layer, a convolution layer, and a dense layer.
  • each layer has several nodes therein, and is divided according to an input/output configuration method between nodes (inputs) of the previous layer and nodes (outputs) in each layer.
  • the fully connected layer mainly used in this embodiment may be represented by the structure shown in FIG. 7 .
  • reference numeral 710 denotes a node
  • reference numeral 720 denotes a previous layer
  • reference numeral 730 denotes a current layer. That is, as shown in FIG. 7 , a layer in which all possible connections exist between nodes of the previous layer 720 and nodes of the current layer 730 is referred to as a fully connected layer.
  • the artificial neural network is limited to being implemented through multiple perceptrons or fully connected layers, but is not limited thereto, and may be implemented through structures other than multiple perceptrons or fully connected layers.
  • FIG. 8 is a diagram for explaining the structure and implementation of a multi-perceptron.
  • one node 810 calculates the nodes and weights of the previous layer as a weighted sum and then applies a kind of threshold value. Outputs the result value as 1, otherwise 0.
  • the process (or function) for applying the threshold value is called an activation function.
  • a neuron does not immediately emit an output when receiving an input, but emits an output only when the input is accumulated and grows to a certain level. This function is called an activation function.
  • a bias b indicating a direct bias may be added to the activation function.
  • One node 810 may be expressed by Equation 3 below.
  • one layer 820 may include n nodes of an input layer and m nodes of a hidden layer.
  • one layer 820 may be expressed by Equation 4 below.
  • multiple perceptrons may be composed of an input layer, a plurality of hidden layers, and an output layer.
  • the remaining capacity of the battery can be estimated only from data from an arbitrary charging start time to the charging end. Specifically, when the learning method of the present invention is utilized, the remaining battery capacity can be estimated at a faster rate than in the prior art.
  • the computing device for estimating the battery capacity based on constant current charging using the learning method of the present invention can be utilized in portable electronic products such as laptops, video cameras, and portable phones, thereby contributing to battery life management and prevention of accidents due to battery short. have.

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Abstract

La présente invention concerne un procédé et un dispositif d'estimation de capacité de batterie basée sur un réseau neuronal, et fournit un procédé d'entraînement d'un réseau neuronal dans un dispositif informatique pour une estimation de capacité de batterie basée sur la charge à courant constant, le procédé comprenant les étapes consistant : à collecter les capacités restantes d'une batterie d'un point de départ de charge arbitraire à un point où une tension maximale est atteinte pendant la charge ; et à entrer les capacités restantes collectées dans le réseau neuronal pour entraîner le réseau neuronal.
PCT/KR2021/000136 2020-03-30 2021-01-06 Procédé et dispositif d'estimation de capacité de batterie basée sur un réseau neuronal WO2021201387A1 (fr)

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