KR20230143889A - Battery cell diagnosing apparatus and operating method of the same - Google Patents

Abstract

A battery cell diagnosis device according to an embodiment disclosed in this document includes charge/discharge condition information of the battery cell, voltage information of the battery cell in a charge/discharge cycle section within a set number of times, and information on the battery cell in the charge/discharge cycle section. It may include an information acquisition unit that acquires battery cell diagnostic information including dQ/dV information related to capacity and voltage information of the battery cell, and a controller that diagnoses the future state of the battery cell based on the battery cell diagnostic information. there is.

Description

Battery cell diagnostic device and operating method thereof {BATTERY CELL DIAGNOSING APPARATUS AND OPERATING METHOD OF THE SAME}

Embodiments disclosed in this document relate to a battery cell diagnostic device and a method of operating the same.

Recently, research and development on secondary batteries has been actively conducted. Here, the secondary battery is a battery capable of charging and discharging, and includes both conventional Ni/Cd batteries, Ni/MH batteries, etc., and recent lithium ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni/Cd batteries, Ni/MH batteries, etc. In addition, lithium-ion batteries can be manufactured in small and light sizes, so they are used as a power source for mobile devices. Recently, its range of use has expanded as a power source for electric vehicles, and it is attracting attention as a next-generation energy storage medium.

Batteries show a tendency to deteriorate as they are repeatedly charged and discharged. For example, as batteries continue to charge and discharge, their capacity deteriorates, their resistance deteriorates, and their remaining lifespan decreases. Additionally, the degree of deterioration and remaining life of a battery varies depending on charging and discharging conditions. When the capacity of a battery deteriorates or its remaining life suddenly decreases, problems may arise in the safety of users using the battery, so a method of diagnosing the deterioration of the battery and the rapid decline in its lifespan in advance is necessary.

One purpose of the embodiments disclosed in this document is to provide a battery cell diagnosis device capable of diagnosing the future state of a battery cell and a method of operating the same.

One purpose of the embodiments disclosed in this document is to provide a battery capable of diagnosing the remaining life of the battery cell, capacity degradation of the battery cell, and resistance degradation of the battery cell based on information about the battery cell in a charge/discharge cycle section within a set number of times. The object is to provide a cell diagnosis device and a method of operating the same.

The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

A battery cell diagnosis device according to an embodiment disclosed in this document includes charge/discharge condition information of the battery cell, voltage information of the battery cell in a charge/discharge cycle section within a set number of times, and information on the battery cell in the charge/discharge cycle section. It may include an information acquisition unit that acquires battery cell diagnostic information including dQ/dV information related to capacity and voltage information of the battery cell, and a controller that diagnoses the future state of the battery cell based on the battery cell diagnostic information. there is.

In one embodiment, the controller may diagnose at least one of the remaining life of the battery cell, capacity degradation of the battery cell, and resistance degradation of the battery cell.

In one embodiment, the controller may classify the battery cells into a first class having a first set number of charge/discharge cycles or more, a second class that is less than the first set number of charge/discharge cycles but more than a second set number of charge/discharge cycles, and the second class. The remaining lifespan of the battery cell can be diagnosed by learning to classify it into a third class that is less than the set number of charge/discharge cycles.

In one embodiment, the controller may first classify battery cells included in the third class, and then classify battery cells included in the first class and the second class.

In one embodiment, the controller may diagnose capacity degradation of the battery cell based on the first class, the second class, and the third class.

In one embodiment, resistance degradation of the battery cell may be diagnosed based on the first class, the second class, and the third class.

In one embodiment, the controller includes a first slope of the first phase, a second slope of the second phase, a third slope of the third phase, a first time to change from the first phase to the second phase, and the first slope of the second phase. Capacity degradation of the battery cell can be diagnosed by learning the second time that changes from the second phase to the third phase.

In one embodiment, the controller classifies the battery cell into a first class, a second class, or a third class based on the remaining life of the battery cell, and the first slope according to the class to which the battery cell belongs, The second slope, the third slope, the first time, and the second time can be learned.

In one embodiment, the controller includes charging and discharging condition information of the battery cell, voltage information of the battery cell after charging and discharging the set number of times, dQ/dV information of the battery cell after charging and discharging the set number of times, and Based on the number of charge and discharge cycles, resistance degradation of the battery cell can be learned and diagnosed.

In one embodiment, the setting number of times may be 100.

In one embodiment, the controller may diagnose the future state of the battery cell based on a support vector machine.

A method of operating a battery cell diagnosis device according to an embodiment disclosed in this document includes charge/discharge condition information of a battery cell, voltage information of the battery cell in a charge/discharge cycle section within a set number of times, and information on the charge/discharge cycle section in the charge/discharge cycle section. Obtaining battery cell diagnostic information including dQ/dV information related to the capacity of the battery cell and voltage information of the battery cell, and diagnosing a future state of the battery cell based on the battery cell diagnostic information. can do.

In one embodiment, the step of diagnosing the future state of the battery cell includes: a first class that is more than a first set number of charge/discharge cycles, and a second class that is less than the first set number of charge/discharge cycles and is more than a second set number of charge/discharge cycles. and diagnosing the remaining lifespan of the battery cell by learning to classify it into a third class that is less than the second set number of charge/discharge cycles.

In one embodiment, diagnosing the future state of the battery cell may further include diagnosing capacity degradation of the battery cell based on the first class, the second class, and the third class. .

In one embodiment, the step of diagnosing the future state of the battery cell includes: a first slope of the first phase, a second slope of the second phase, a third slope of the third phase, and the second slope in the first phase. Capacity degradation of the battery cell can be diagnosed by learning the first time that changes from and the second time that changes from the second phase to the third phase.

In one embodiment, diagnosing the future state of the battery cell may further include diagnosing resistance degradation of the battery cell based on the first class, the second class, and the third class. .

The battery cell diagnosis device and its operating method according to an embodiment disclosed in this document can diagnose the future state of a battery cell.

The battery cell diagnosis device and its operating method according to an embodiment disclosed in this document determine the future state of the battery cell based on the voltage of the battery cell and the differential value of the capacity voltage of the battery cell in a charge/discharge cycle section within a set number of times. It can be diagnosed.

The battery cell diagnosis device and its operating method according to an embodiment disclosed in this document can pre-diagnose the remaining life of the battery cell, capacity degradation of the battery cell, and resistance degradation of the battery cell.

The battery cell diagnosis device and its operating method according to an embodiment disclosed in this document can classify the class by diagnosing the remaining lifespan of the battery cell, and based on the classified class, capacity degradation of the battery cell or battery cell Resistance degradation can be diagnosed in advance.

The battery cell diagnosis device and its operating method according to an embodiment disclosed in this document are based on a support vector machine based on the voltage of the battery cell and the differential value of the capacity voltage of the battery cell in a charge/discharge cycle section within a set number of times. Machine learning can be used to diagnose the condition of battery cells in advance.

In addition, various effects that can be directly or indirectly identified through this document may be provided.

1 is a block diagram showing a battery cell diagnosis device according to an embodiment disclosed in this document.
FIG. 2 is a diagram showing a voltage graph and a capacity voltage differential value graph of a battery cell according to an embodiment disclosed in this document.
3 is a graph related to the remaining lifespan of a battery cell according to an embodiment disclosed in this document.
Figure 4 is a diagram showing an example of diagnosing the remaining life of a battery cell according to an embodiment disclosed in this document.
Figure 5 is a graph related to capacity degradation of a battery cell according to an embodiment disclosed in this document.
Figure 6 is a diagram showing an example of diagnosing capacity degradation of a battery cell according to an embodiment disclosed in this document.
Figure 7 is a diagram showing an example of diagnosing resistance degradation of a battery cell according to an embodiment disclosed in this document.
Figure 8 is a flowchart showing a method of operating a battery cell diagnosis device according to an embodiment disclosed in this document.
Figure 9 is a flowchart specifically showing a method of operating a battery cell diagnosis device according to an embodiment disclosed in this document.
Figure 10 is a block diagram showing the hardware configuration of a computing system for performing a method of operating a battery cell diagnosis device according to an embodiment disclosed in this document.

Hereinafter, embodiments disclosed in this document will be described in detail through illustrative drawings. When adding reference numerals to components in each drawing, it should be noted that identical components are given the same reference numerals as much as possible even if they are shown in different drawings. Additionally, in describing the embodiments disclosed in this document, if it is determined that detailed descriptions of related known configurations or functions impede understanding of the embodiments disclosed in this document, the detailed descriptions will be omitted.

In describing the components of the embodiment disclosed in this document, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only used to distinguish the component from other components, and the nature, sequence, or order of the component is not limited by the term. Additionally, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which the embodiments disclosed in this document belong. . Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted in an ideal or excessively formal sense unless explicitly defined in the present application. No.

1 is a block diagram showing a battery cell diagnosis device according to an embodiment disclosed in this document.

Referring to FIG. 1, the battery cell diagnosis device 100 according to an embodiment disclosed in this document may include an information acquisition unit 110 and a controller 120.

The information acquisition unit 110 may acquire charging/discharging condition information of the battery cell. For example, the information acquisition unit 110 may acquire voltage and current information when charging a battery cell and voltage and current information when discharging a battery cell.

The information acquisition unit 110 may acquire voltage information of the battery cell in a charge/discharge cycle section within a set number of times. For example, the information acquisition unit 110 may obtain a voltage and capacity graph of a battery cell in a charge/discharge cycle section within a set number of times. Depending on the embodiment, the number of settings may be 100. However, it is not limited to this, and the set number of times may have a natural number. Depending on the embodiment, the information acquisition unit 110 may obtain the initial voltage of the battery cell.

The information acquisition unit 110 may acquire dQ/dV (capacity voltage differential value) information of the battery cell in a charge/discharge cycle section within a set number of times. For example, the information acquisition unit 110 may obtain dQ/dV information by differentiating the obtained capacitance voltage graph. For another example, the information acquisition unit 110 may acquire capacity voltage differential value information calculated from another device. Depending on the embodiment, the information acquisition unit 110 may acquire the peak dQ/dV value. Depending on the embodiment, the information acquisition unit 110 may obtain the initial dQ/dV value of the battery cell.

FIG. 2 is a diagram showing a voltage graph and a capacity voltage differential value graph of a battery cell according to an embodiment disclosed in this document.

Referring to FIG. 2 , the information acquisition unit 110 may obtain a voltage information graph 210 of a battery cell. For example, the battery cell voltage information graph 210 may include voltage information of a plurality of battery cells. For another example, the x-axis of the battery cell voltage information graph 210 may represent the capacity of the battery cell, and the y-axis may represent the voltage of the battery cell.

The information acquisition unit 110 may acquire the dQ/dV information graph 220 of the battery cell. For example, the information acquisition unit 110 may obtain the dQ/dV information graph 220 of the battery cell by differentiating the voltage information graph 210 of the battery cell. Depending on the embodiment, the x-axis of the dQ/dV information graph 220 of the battery cell may represent the voltage of the battery cell, and the y-axis may represent the capacity voltage differential value (dQ/dV value) of the battery cell.

Referring again to FIG. 1, the controller 120 provides information about the charging and discharging conditions of the battery cell, voltage information of the battery cell in a charging and discharging cycle section within a set number of times, and the capacity of the battery cell and the battery in a charging and discharging cycle section within a set number of times. The future state of a battery cell can be diagnosed based on dQ/dV information related to the cell's voltage information. For example, dQ/dV information may include differential information of battery cell capacity and voltage. For example, the controller 120 learns the charge/discharge condition information of the battery cell, voltage information of the battery cell in a charge/discharge cycle section within a set number of times, and dQ/dV information of the battery cell in a charge/discharge cycle section within a set number of times. This allows the future state of the battery cell to be diagnosed in advance. For another example, the controller 120 may diagnose at least one of the remaining life of the battery cell, capacity degradation of the battery cell, and resistance degradation of the battery cell.

According to an embodiment, the controller 120 may diagnose the future state of the battery cell based on a support vector machine.

The controller 120 may learn to classify battery cells into a plurality of classes based on the remaining lifespan and diagnose the remaining lifespan of the battery cells. For example, the controller 120 may classify the battery cell into a first class having a first set number of charge/discharge cycles or more, a second class having a number of charge/discharge cycles less than the first set but more than a second set charge/discharge cycle number, and a second set charge/discharge cycle number. By learning to classify the battery cell into a third class with less than the number of cycles, the remaining lifespan of the battery cell can be diagnosed.

Depending on the embodiment, the first set number of charge/discharge cycles may be 2000 cycles, and the second set number of cycles may be 1000 cycles.

Depending on the embodiment, the controller 120 may first classify battery cells included in the third class, and then classify battery cells included in the first class and the second class.

Depending on the embodiment, the charge/discharge cycle may be a Full Equivalent Cycle.

3 is a graph related to the remaining lifespan of a battery cell according to an embodiment disclosed in this document.

Referring to FIG. 3, the controller 120 may classify battery cells into first class, second class, and third class. For example, a battery cell included in the first class may be capable of undergoing more than 2000 charge/discharge cycles, and in this case, the probability of a sharp drop in SOH (state of health) before reaching 80 percent may be less than 1 percent. there is. For another example, a battery cell included in the second class may be capable of undergoing charge/discharge cycles of more than 1000 cycles but less than 2000 cycles, and in this case, the probability of a sharp drop in SOH before reaching 80 percent SOH may be less than 10 percent. there is. As another example, a battery cell included in the third class may be capable of undergoing less than 1000 charge/discharge cycles, and in this case, the probability of a sharp drop in SOH before reaching 80 percent SOH may be less than 100 percent.

Figure 4 is a diagram showing an example of diagnosing the remaining life of a battery cell according to an embodiment disclosed in this document.

Referring to FIG. 4 , the controller 120 may classify the third class through the first classification model 410 based on the charging and discharging conditions of the battery cell, voltage information of the battery cell, and initial dQ/dV information. For example, the controller 120 may learn the first classification model 410.

Additionally, the controller 120 may classify battery cells into a first class and a second class through the second classification model 420. For example, the controller 120 may learn the second classification model 420.

Depending on the embodiment, the controller 120 may learn the first classification model 410 and the second classification model 420 based on accuracy, precision, and reproducibility. For example, accuracy may indicate the number of cases that the classification model (410. 420) correctly guessed among all the number of classification cases. As another example, precision may indicate how many of the number of cases classified as correct by the classification model 410 and 420 are actually correct. As another example, reproducibility may indicate how many cases the classification models 410 and 420 classified as correct out of the number of cases that should be classified as correct.

According to an embodiment, rather than classifying battery cells into a first class, a second class, and a third class based only on the charging/discharging conditions of the battery cells, the controller 120 classifies the charging/discharging conditions of the battery cells and the voltage of the battery cells. Classification of battery cells into first class, second class, and third class using both information and initial dQ/dV information may be more accurate.

Referring again to FIG. 1, the controller 120 can diagnose capacity degradation of battery cells. For example, the controller 120 may learn and diagnose the capacity degradation of a battery cell based on the 3-phase capacity degradation pattern of a Nickel Cobalt Manganese (NCM) battery. For example, the controller 120 may configure the first slope of the first phase, the second slope of the second phase, the third slope of the third phase, the first time to change from the first phase to the second phase, and the first slope of the second phase. Capacity degradation of the battery cell can be diagnosed by learning the second time when the second phase changes from the third phase to the third phase.

According to an embodiment, the controller 120 may first classify the class of the battery cell and diagnose capacity degradation of the battery cell based on the classified class. For example, the controller 120 may classify battery cells into a first class, a second class, and a third class, and diagnose capacity degradation of the battery cell using different models for each class. That is, the controller 120 may diagnose capacity degradation of the battery cell based on the first class, second class, and third class.

Figure 5 is a graph related to capacity degradation of a battery cell according to an embodiment disclosed in this document.

Figure 5 shows a 3-phase capacity degradation graph 510 according to the progress of the charge and discharge cycle of the battery cell.

Referring to FIG. 5, since battery cell capacity degradation occurs in three phases, the controller 120 can diagnose battery cell capacity degradation by learning the slope and time for each phase. For example, the controller 120 may set the first slope (a ₁ ), second slope of the second phase (a ₂ ), third slope of the third phase (a ₃ ), first time changing from the first phase to the second phase (t ₁ ), from the second phase to the third phase You can learn the second time (t ₂ ) that changes to . In this case, the controller 120 uses the learned first slope (a ₁ ), second slope (a ₂ ), third slope (a ₃ ), first time (t ₁ ), and second time (t ₂ ). Based on this, capacity degradation of battery cells can be diagnosed.

Figure 6 is a diagram showing an example of diagnosing capacity degradation of a battery cell according to an embodiment disclosed in this document.

Referring to FIG. 6, the controller 120 can diagnose capacity degradation of battery cells classified through the classification model 610. For example, the classification model 610 may include the first classification model 410 and/or the second classification model 420 shown in FIG. 4 . For another example, the controller 120 may classify battery cells into a first class and a second class through the classification model 610.

The controller 120 may diagnose capacity degradation of a battery cell based on the classified class. For example, the controller 120 determines the first slope (a ₁ ), the second slope (a ₂ ), and the third slope (a ₃ ) of the battery cells included in the first class through the first regression model 620. , the first time (t ₁ ) and the second time (t ₂ ) can be learned. In addition, the controller 120 determines the first slope (a ₁ ), the second slope (a ₂ ), the third slope (a ₃ ), and the battery cells included in the second class through the second regression model 630. You can learn the first time (t ₁ ) and the second time (t ₂ ). In this case, parameters learned in relation to the battery cells included in the first class (e.g., first slope (a ₁ ), second slope (a ₂ ), third slope (a ₃ ), first time (t ₁ ), a second time (t ₂ )) and parameters learned in relation to battery cells included in the second class (e.g., first slope (a ₁ ), second slope (a ₂ ), third slope (a ₃ ), the first time (t ₁ ), and the second time (t ₂ )) may be different from each other.

Referring again to FIG. 1, the controller 120 may diagnose resistance degradation of a battery cell. For example, the controller 120 is based on the charging/discharging condition information of the battery cell, the voltage information of the battery cell after charging and discharging a set number of times, the dQ/dV information of the battery cell after charging and discharging a set number of times, and the charging and discharging number. Thus, the resistance degradation of the battery cell can be learned and diagnosed. According to the embodiment, the controller 120 diagnoses resistance degradation of the battery cell based on the peak value of the difference between the dQ/dV value of the battery cell and the dQ/dV value of the initial battery cell after charging and discharging a set number of times. You can.

According to an embodiment, the controller 120 may first classify the class of the battery cell and diagnose resistance degradation of the battery cell based on the classified class. For example, the controller 120 may classify battery cells into a first class, a second class, and a third class, and diagnose resistance degradation of the battery cell using different models for each class. That is, the controller 120 may diagnose resistance degradation of the battery cell based on the first class, second class, and third class.

Figure 7 is a diagram showing an example of diagnosing resistance degradation of a battery cell according to an embodiment disclosed in this document.

Referring to FIG. 7 , the controller 120 can diagnose resistance degradation of battery cells classified through the classification model 710. For example, the classification model 710 may include the first classification model 410 and/or the second classification model 420 shown in FIG. 4 . For another example, the controller 120 may classify battery cells into a first class and a second class through the classification model 710.

The controller 120 may diagnose resistance degradation of a battery cell based on the classified class. For example, the controller 120 may learn resistance degradation of battery cells included in the first class through the third regression model 720. Additionally, the controller 120 may learn resistance degradation of battery cells included in the second class through the fourth regression model 730. In this case, the parameters learned in relation to the battery cells included in the first class and the parameters learned in relation to the battery cells included in the second class may be different from each other. That is, the third regression model 720 and the fourth regression model 730 may be different from each other.

A battery cell diagnosis device according to an embodiment disclosed in this document can diagnose the future state of a battery cell.

The battery cell diagnosis device according to an embodiment disclosed in this document can diagnose the future state of the battery cell based on the voltage of the battery cell and the differential value of the capacity voltage of the battery cell in a charge/discharge cycle section within a set number of times.

The battery cell diagnosis device according to an embodiment disclosed in this document can pre-diagnose the remaining life of the battery cell, the capacity degradation of the battery cell, and the resistance degradation of the battery cell.

The battery cell diagnosis device according to an embodiment disclosed in this document can classify the class by pre-diagnosing the remaining life of the battery cell, and pre-diagnoses capacity deterioration of the battery cell or resistance deterioration of the battery cell based on the classified class. can do.

The battery cell diagnosis device according to an embodiment disclosed in this document performs machine learning based on a support vector machine based on the voltage of the battery cell and the differential value of the capacity voltage of the battery cell in a charging/discharging cycle section within a set number of times to determine the battery cell's capacity. The condition can be diagnosed in advance.

Figure 8 is a flowchart showing a method of operating a battery cell diagnosis device according to an embodiment disclosed in this document.

Referring to FIG. 8, the operating method of the battery cell diagnosis device 100 according to an embodiment disclosed in this document includes charging and discharging condition information of the battery cell, voltage information of the battery cell in a charging and discharging cycle section within a set number of times, and Obtaining dQ/dV information related to the capacity and voltage information of the battery cell in a charge/discharge cycle section within a set number of times (S110) and diagnosing the future state of the battery cell based on the battery cell diagnosis information (S110) S120) may be included.

Obtain dQ/dV information related to battery cell charge/discharge condition information, battery cell voltage information in a charge/discharge cycle section within a set number of times, and battery cell capacity and battery cell voltage information in a charge/discharge cycle section within a set number of times. In step S110, the information acquisition unit 110 collects information on the charging and discharging conditions of the battery cell, voltage information of the battery cell in a charging and discharging cycle section within a set number of times, and capacity and capacity of the battery cell in a charging and discharging cycle section within a set number of times. dQ/dV information related to the voltage information of the battery cell can be obtained. For example, dQ/dV information may include differential information of battery cell capacity and voltage.

In the step (S120) of diagnosing the future state of the battery cell based on the battery cell diagnosis information, the controller 120 determines the charge/discharge condition information of the battery cell, the voltage information of the battery cell in the charge/discharge cycle section within the set number of times, and the set number of times. The future state of the battery cell can be diagnosed based on the dQ/dV information of the battery cell within the charging/discharging cycle section. For example, the controller 120 is based on charge/discharge condition information of the battery cell, voltage information of the battery cell in a charge/discharge cycle section within a set number of times, and dQ/dV information of the battery cell in a charge/discharge cycle section within a set number of times. Thus, the future state of the battery cell can be learned and diagnosed. For another example, the controller 120 may diagnose at least one of the remaining life of the battery cell, capacity degradation of the battery cell, and resistance degradation of the battery cell.

According to an embodiment, the controller 120 may diagnose the future state of the battery cell based on a support vector machine.

Figure 9 is a flowchart specifically showing a method of operating a battery cell diagnosis device according to an embodiment disclosed in this document.

Referring to FIG. 9, the operating method of the battery cell diagnosis device 100 according to an embodiment disclosed in this document includes a first class in which the number of charge/discharge cycles is more than the first setting, and a second class in which the number of charge/discharge cycles is less than the first setting. A step (S210) of diagnosing the remaining life of the battery cell by learning to classify it into a second class with more than the number of charge/discharge cycles and a third class with less than the second set number of charge/discharge cycles, the first class, the second class, and the third class. It may include a step of diagnosing capacity degradation of the battery cell based on (S220) and a step of diagnosing resistance degradation of the battery cell based on the first class, second class, and third class (S230). Depending on the embodiment, steps S210 to S230 may be included in step S120 of FIG. 8.

The battery is learned to be classified into a first class that is more than the first setting number of charge/discharge cycles, a second class that is less than the first set number of charge/discharge cycles but is more than the second set number of charge/discharge cycles, and a third class that is less than the second set number of charge/discharge cycles. In the step of diagnosing the remaining life of the cell (S210), the controller 120 classifies the battery cell into a first class having a first set number of charge/discharge cycles or more, a class that is less than the first set number of charge/discharge cycles and more than a second set number of charge/discharge cycles. By learning to classify into class 2 and class 3, which is less than the second set number of charge/discharge cycles, the remaining lifespan of the battery cell can be diagnosed.

Depending on the embodiment, the first set number of charge/discharge cycles may be 2000 cycles, and the second set number of cycles may be 1000 cycles.

Depending on the embodiment, the controller 120 may first classify battery cells included in the third class, and then classify battery cells included in the first class and the second class.

In the step of diagnosing capacity degradation of the battery cell based on the first class, second class, and third class (S220), the controller 120 may diagnose capacity degradation of the battery cell based on the classified class of the battery cell. there is. For example, the controller 120 may configure the first slope of the first phase, the second slope of the second phase, the third slope of the third phase, the first time to change from the first phase to the second phase, and the first slope of the second phase. Capacity degradation of the battery cell can be diagnosed by learning the second time when the second phase changes from the third phase to the third phase.

In the step of diagnosing resistance degradation of the battery cell based on the first class, second class, and third class (S230), the controller 120 may diagnose resistance degradation of the battery cell based on the classified class of the battery cell. there is.

Figure 10 is a block diagram showing the hardware configuration of a computing system for performing a method of operating a battery cell diagnosis device according to an embodiment disclosed in this document.

Referring to FIG. 10, the computing system 1000 according to an embodiment disclosed in this document may include an MCU 1010, a memory 1020, an input/output I/F 1030, and a communication I/F 1040. there is.

The MCU 1010 operates various programs stored in the memory 1020 (e.g., a battery pack voltage or current collection program, a relay control program included in the battery pack, a battery cell remaining life calculation program, and a battery cell capacity deterioration diagnosis program. , a battery cell resistance degradation determination program, etc.) are executed, and various information including the remaining lifespan information of the battery cell, battery cell capacity degradation information, and battery cell resistance degradation information are processed through these programs, and the above-mentioned FIG. 1 It may be a processor that performs the battery cell diagnosis device shown in .

The memory 1020 can store various programs related to battery log information collection and diagnosis. In addition, the memory 1020 includes battery current, voltage, charge/discharge condition information, battery cell voltage information in a charge/discharge cycle section within a set number of times, dQ/dV information of a battery cell in a charge/discharge cycle section within a set number of times, etc. Various information can be stored.

A plurality of such memories 1020 may be provided as needed. Memory 1020 may be volatile memory or non-volatile memory. The memory 1020 as a volatile memory may use RAM, DRAM, SRAM, etc. The memory 1020 as a non-volatile memory may be ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of memories 1020 listed above are merely examples and are not limited to these examples.

The input/output I/F (1030) is an interface that connects the MCU (1010) with input devices (not shown) such as a keyboard, mouse, and touch panel, and output devices such as a display (not shown) to transmit and receive data. can be provided.

The communication I/F 1040 is a component that can transmit and receive various data with a server, and may be various devices that can support wired or wireless communication. For example, the battery cell diagnosis device receives relay control programs included in the battery pack or current, current, dQ/dV values, charge/discharge condition information, and charge/discharge condition information of various battery packs from a separately provided external server through the communication I/F (1040). Remaining life, capacity degradation, or resistance degradation information can be transmitted and received.

In this way, the computer program according to an embodiment disclosed in this document may be recorded in the memory 1020 and processed by the MCU 1010, so that it may be implemented as a module that performs each function shown in FIG. 1, for example. there is.

The above description is merely an illustrative explanation of the technical idea disclosed in this document, and those skilled in the art in the technical field to which the embodiments disclosed in this document belong will understand without departing from the essential characteristics of the embodiments disclosed in this document. Various modifications and variations will be possible.

Accordingly, the embodiments disclosed in this document are not intended to limit the technical ideas disclosed in this document, but rather to explain them, and the scope of the technical ideas disclosed in this document is not limited by these embodiments. The scope of protection of the technical ideas disclosed in this document shall be interpreted in accordance with the claims below, and all technical ideas within the equivalent scope shall be interpreted as being included in the scope of rights of this document.

100: Battery cell diagnostic device
110: Information acquisition department
120: controller
1000: Computing system
1010:MCU
1020: memory
1030: Input/output I/F
1040: Communication I/F

Claims (16)

Battery cell charge/discharge condition information, voltage information of the battery cell in a charge/discharge cycle section within a set number of times, and dQ/dV information related to the capacity of the battery cell and voltage information of the battery cell in the charge/discharge cycle section. An information acquisition unit that acquires battery cell diagnostic information including; and
a controller that diagnoses a future state of the battery cell based on the battery cell diagnosis information; A battery cell diagnostic device comprising a. According to claim 1,
The controller is,
A battery cell diagnostic device characterized in that it diagnoses at least one of the remaining life of the battery cell, capacity degradation of the battery cell, and resistance degradation of the battery cell. According to claim 2,
The controller is,
A first class in which the battery cell has a first set number of charge/discharge cycles or more, a second class in which the number of charge/discharge cycles is less than the first set but more than a second set number of charge/discharge cycles, and a third class that is less than the second set number of charge/discharge cycles. A battery cell diagnostic device characterized in that it learns to classify and diagnose the remaining life of the battery cell. According to claim 3,
The controller is,
First classify the battery cells included in the third class,
A battery cell diagnosis device, characterized in that the battery cells included in the first class and the second class are later classified. According to claim 3,
The controller is,
A battery cell diagnostic device, characterized in that diagnosing capacity degradation of the battery cell based on the first class, the second class, and the third class. According to claim 3,
A battery cell diagnostic device characterized in that it diagnoses resistance degradation of the battery cell based on the first class, the second class, and the third class. According to claim 2,
The controller is,
The first slope of the first phase, the second slope of the second phase, the third slope of the third phase, the first time changing from the first phase to the second phase, and the changing time from the second phase to the third phase. A battery cell diagnostic device characterized in that it diagnoses capacity degradation of the battery cell by learning a second time. According to claim 7,
The controller is,
Classifying the battery cells into a first class, a second class, or a third class based on the remaining life of the battery cell,
A battery cell diagnosis device, characterized in that learning the first slope, the second slope, the third slope, the first time, and the second time according to the class to which the battery cell belongs. According to claim 2,
The controller is,
The battery cell based on charge/discharge condition information of the battery cell, voltage information of the battery cell after charge/discharge performed by the set number of times, dQ/dV information of the battery cell after charge/discharge performed by the set number of times, and charge/discharge number of the battery cell. A battery cell diagnostic device characterized in that it is diagnosed by learning the resistance degradation of. According to claim 1,
A battery cell diagnostic device, characterized in that the setting number of times is 100. According to claim 1,
The controller is,
A battery cell diagnosis device characterized in that it diagnoses the future state of the battery cell based on a support vector machine. Battery cell charge/discharge condition information, voltage information of the battery cell in a charge/discharge cycle section within a set number of times, and dQ/dV information related to the capacity of the battery cell and voltage information of the battery cell in the charge/discharge cycle section. Obtaining battery cell diagnostic information including; and
diagnosing a future state of the battery cell based on the battery cell diagnosis information; A method of operating a battery cell diagnostic device comprising: According to claim 12,
The step of diagnosing the future state of the battery cell is,
Learning to classify into a first class that is more than the first set number of charge/discharge cycles, a second class that is less than the first set number of charge/discharge cycles and more than the second set number of charge/discharge cycles, and a third class that is less than the second set number of charge/discharge cycles. diagnosing the remaining lifespan of the battery cell; A method of operating a battery cell diagnostic device comprising: According to claim 13,
The step of diagnosing the future state of the battery cell is,
diagnosing capacity degradation of the battery cell based on the first class, the second class, and the third class; A method of operating a battery cell diagnostic device further comprising: According to claim 14,
The step of diagnosing the future state of the battery cell is,
The first slope of the first phase, the second slope of the second phase, the third slope of the third phase, the first time changing from the first phase to the second phase, and the changing time from the second phase to the third phase. A method of operating a battery cell diagnosis device, characterized in that diagnosing capacity degradation of the battery cell by learning a second time. According to claim 13,
The step of diagnosing the future state of the battery cell is,
diagnosing resistance degradation of the battery cell based on the first class, the second class, and the third class; A method of operating a battery cell diagnostic device further comprising: