KR101949449B1 - Method and apparatus for estimating battery life - Google Patents

Abstract

A method and an apparatus for estimating a battery life are disclosed. The apparatus for estimating a battery life sets an initial estimated lifetime of a battery; receives and accumulates the cell voltage at the time of charging or discharging at least one cell belonging to the battery; allocates cells to a group based on a cell voltage indicating a different type of change for each group classified on the basis of the cause of deterioration of the cells; determines the relationship between the change range of the state of charge (SOC) of the cells and the use period for each group; and updates the initial estimated lifetime with a value obtained by averaging the use period when the change range of the state of charge (SOC) of the cells becomes the predetermined reference range for each group.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001]

The present invention relates to a battery life predicting method and apparatus, and more particularly, to a battery life predicting method and apparatus using an ESS (Energy) system using various IT technologies such as a Big data system, a cloud system, and a machine learning. Storage system) or an electric vehicle and the like.

Batteries used in ESS and electric vehicles are available for use by the manufacturer. However, this is only information provided by the manufacturer uniformly, but the actual serviceable life is not constant depending on the environment in which the battery is used, the number of charge / discharge cycles, and the like.

Japanese Patent Application Laid-Open No. 10-2016-0090226 "Method and Apparatus for Estimating Battery Life"

Disclosure of Invention Technical Problem [8] The present invention provides a method and apparatus for accurately predicting the life of a battery, as well as a cause of deterioration affecting battery life.

According to another aspect of the present invention, there is provided a method of predicting battery life, the method comprising: setting an initial predicted life span of a battery; Receiving and accumulating a cell voltage during charging or discharging of at least one cell belonging to the battery; Allocating cells to any one group based on a cell voltage representing a different type of change for each group classified on the basis of a deterioration cause of the cell; Determining a relationship between a change range of a state of charge (SOC) of a cell and a use period for each group; And updating the initial predicted life span to a value obtained by averaging a use period when a change range of the state of charge of the cells becomes a predetermined reference range for each group.

According to an aspect of the present invention, there is provided an apparatus for predicting battery life, the apparatus comprising: a setting unit configured to set an initial predicted lifetime of a battery; A storage unit for receiving and accumulating a cell voltage during charging or discharging of at least one cell belonging to the battery; A grouping unit for assigning cells to any one group based on a cell voltage indicating a different type of change for each group classified based on a cause of deterioration of the cell; (SOC) of each cell and the usage period, and calculates a value obtained by averaging the usage period when the variation range of the charging state of the cell becomes the preset reference range for each group And the setting unit updates the initial predicted life span with the averaged value.

According to the present invention, battery life can be predicted accurately. In addition, it is possible to identify the cause of deterioration that affects battery life and take appropriate action accordingly to prolong battery life.

FIG. 1 and FIG. 2 are views showing an example of a battery to be a battery life predicting target according to an embodiment of the present invention; FIG.
3 illustrates a schematic structure of an overall system for battery life prediction according to an embodiment of the present invention;
FIG. 4 illustrates a battery life predicting method according to an embodiment of the present invention. FIG.
5 to 10 are graphs showing the cell voltage and the like according to the deterioration cause of the lithium ion battery,
11 is a diagram illustrating an example of a method of predicting battery life based on an average usage period of each group according to an embodiment of the present invention.
12 is a diagram illustrating an example of a method of analyzing a correlation between a group of causes of battery deterioration and a battery use environment according to an embodiment of the present invention,
13 is a diagram illustrating an example of a configuration of a battery life predicting apparatus according to an embodiment of the present invention.

Hereinafter, a battery life predicting method and apparatus according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 and FIG. 2 are views showing an example of a battery as a battery life predicting target according to an embodiment of the present invention.

Referring to FIGS. 1 and 2, the battery may be composed of one battery module 100, 200 or a plurality of battery modules. The battery modules 100 and 200 may include at least one or more cells 110, 21 and 212 and a battery management system (BMS) 120 and 220. For example, the high capacity battery module 100 may be composed of one cell 110. Or the battery module 200 may include a plurality of cells 210 and 212 in series or in parallel.

The BMSs 120 and 220 of the respective battery modules 100 and 200 are connected to the battery modules 100 and 210 in a variety of states such as a state of charge (SOC), a charging rate, a discharge rate, Information can be grasped. In addition, according to the embodiment, as shown in FIGS. 5 to 10, the PE voltage which is the potential difference between the anode and the lithium metal of the cell, the NE voltage which is the potential difference between the cathode of the cell and the lithium metal, and the like can be grasped.

The present embodiment is only one example for facilitating understanding of the present invention, and the present invention is not limited thereto, and battery modules 100 and 200 may be modified into various forms according to the embodiments.

3 is a diagram illustrating a schematic structure of an overall system for battery life prediction according to an embodiment of the present invention.

Referring to FIG. 3, the battery life predicting device 310 is connected to at least one BMS 300 or 302 through a wired / wireless communication network. The battery life predicting device 310 may be implemented as one or a plurality of physical servers or a cloud system.

The battery life predicting device 310 receives information on battery cells from a plurality of BMSs 300 and 302 and stores the information. When there is a large amount of information to be stored, the battery life predicting device 310 can store and analyze information through a big data storage system such as Hadoop. In addition, the battery life predicting apparatus 310 can be applied to a machine learning method for performing the method shown in FIG.

The battery life predicting device 310 predicts the battery life for each battery of the same type (that is, the same manufacturer, model, capacity, etc.). For example, when there are a plurality of battery types, the battery life predicting device 310 receives information on the battery type from the BMSs 300 and 302 and stores the information together with information such as a cell voltage.

Although the BMS 300 or 302 communicates with the battery life predicting device 310 and receives various types of information and transmits the information, the present embodiment is not limited thereto. If the BMSs 300 and 302 do not include a communication module or a configuration for sensing or controlling various information for the following embodiments, the BMSs 300 and 302 may include a separate configuration for this purpose. However, the following embodiments including the present embodiment are shown and described as BMSs 300 and 302 for convenience of explanation.

4 is a diagram illustrating a battery life predicting method according to an embodiment of the present invention.

Referring to FIG. 4, the battery life predicting device sets an initial predicted life time of the battery (S400). For example, the battery life predicting device receives the battery life (for example, 10 years) proposed by the battery manufacturer from the user and sets the battery life to an initial predicted life. The initial predicted life time is set only once for the new battery, and then it is updated through the following process.

First, the battery life predicting device receives and stores a cell voltage at the time of charging or discharging at least one or more cells constituting the battery (S410). For example, the battery life predicting device receives a cell voltage or the like through a BMS of the battery according to a predetermined period such as 1 hour, or whenever the cell voltage changes to a predetermined interval (for example, 1 V) Various methods of receiving a cell voltage and the like such as receiving a cell voltage and the like can be used.

As another example, the battery life predicting device may receive and store battery identification information for identifying each battery, and battery type identification information for identifying the type of each battery together with the cell voltage. For example, if ESS uses 100 batteries, 50 out of 100 batteries are type A batteries and the rest are type B batteries, the battery life predicting device is classified into battery types A and B The identification information and battery identification information uniquely assigned to each of the 100 batteries are stored together with the cell voltage and the like.

As another example, the battery life predicting device may receive and store various information such as charge state (SOC), charge rate, discharge rate, cell temperature, temperature inside and outside the battery together with the cell voltage. As another example, the battery life predicting device may store the reception date of the cell voltage together.

The battery life predicting device allocates each cell to any one of a plurality of predefined groups based on the change state of the voltage of each cell (S420). The plurality of groups may be variously defined depending on the cause of deterioration of the cell. For example, the plurality of groups may be a group of the reduction of lithium ions (Fig. 6), a group of anode active material losses (Figs. 7 and 8), and a group of cathode active material losses (Figs. 9 and 10). The change states of the cell voltages of the respective groups are different from each other. Therefore, the battery life predicting device can assign the cell to any one of the three groups based on the voltage change state of the battery cell received from the BMS.

5 to 10, the cell voltage in the lithium ion reduction group (FIG. 6) is changed in comparison with the reference graph in FIG. 5, and the anode active material loss group (FIGS. 7 and 8) The maximum value portion of the cell voltage changes and the minimum value portion of the cell voltage changes at the time of discharging. In the cathode active material loss group (FIGS. 9 and 10), the maximum value portion of the cell voltage changes during discharging and the minimum value portion of the cell voltage changes during charging. In addition, the type and the number of the groups can be variously modified based on the cause of the battery deterioration according to the embodiment.

The battery life predicting device grasps the relationship between the range of change of the state of charge (SOC) of each cell and the use period for each group (S430). For example, as shown in FIGS. 5 to 10, it can be seen that as the use period increases, the state of charge (SOC) of the cell gradually decreases in the initial 0% to 100% change range. The battery life predicting device maps the change range of the state of charge (SOC) of the cell and the date information, accumulates and manages them, and thereby, the period of time required until the change range of the charge state of each cell becomes a predetermined reference range That is, the period of use).

In step S440, the battery life predicting unit updates the initial predicted lifetime to a value obtained by averaging the usage period until the variation range of the charged state of the cell becomes a predetermined reference range for each group. For example, assuming that the reference range is 80, the battery life predicting device averages the use period until the change range of the charge state of each cell is 80 for each group. If the duration of use of the first battery is 9.6 years and the duration of use of the second battery is 8.6 years, the average of 9.1 years ((9.6 + 8.6) / 2) Life expectancy. A method for updating the initial predicted life span by averaging the usage period of each group will be described again with reference to FIG.

Since the battery life predicting device continuously accumulates various information including the voltage of each cell of the battery and averages the usage period until the change range of the charge state of each group becomes the preset reference range, The more information available, the more accurate the initial predicted life of the battery.

For example, if the initial predicted life time provided by the first manufacturer for the first battery is 10 years, the battery life predicting device initializes 10 years with the initial predicted life time for the first battery. If the period of time required for the change range of the state of charge (SOC) of the first battery to reach the predetermined reference range of 80 is 9.6 years, the battery life predicting device changes the initial predicted life from 10 years to 9.6 years do. If the information on the second battery of the same type is cumulatively accumulated and the usage period is determined to be 8.6 years, the battery life predicting device calculates the average of 9.6 years of the first battery and 8.6 years of the second battery, Lt; RTI ID = 0.0 > of the battery < / RTI >

The battery life predicting device can predict the remaining life of the battery using the initial predicted life time (S450). Although the present embodiment shows that the prediction of the remaining life (S450) is performed after the initial predicted life duration update (S440), the prediction of the remaining life (S450) It is possible at times.

For example, assuming that the range of change in the state of charge is 80, which is a predetermined reference range, and the initial predicted life is 10 years, every time the change range of the state of charge is decreased by 1, (10 years * 365 days / 20) days. Therefore, if the change range of the battery charge state is reduced by 3, the battery life predicting device predicts the remaining battery life of about 8.5 years (10 years - 0.5 years * 3). As another example, if the initial predicted life is updated to 9.1 years, the battery life predicting device predicts that the change range of the charge state is reduced by about 166 (9.1 years * 365 days / 20) every 1 time decrease.

FIGS. 5 to 10 are graphs showing cell voltages and the like according to deterioration causes of a lithium ion battery. Although the present embodiment shows a lithium ion battery, it is not limited thereto. It is possible to grasp the graph of the cell voltage and the like depending on the deterioration cause of the other kinds of batteries. In this case, the shape of the graph may be different from that of FIG. 5 to FIG.

5 is a reference graph showing the voltage of the cell in the initial state of the battery.

Referring to FIG. 5, the state of charge (SOC) of the cell in the initial state of the battery has a variation range of 0% to 100%. The cell voltage 500 has a minimum value at 0% of the state of charge SOC and a maximum value at 100% of state of charge SOC. In addition, a change form for the PE voltage 510 and the NE voltage 520 is also defined.

FIG. 6 is a graph showing the cell voltage and the like in the case of the lithium ion loss of the battery. The graph of FIG. 6 is the same at charging or discharging.

Referring to FIG. 6, there is shown a graph when the total amount of lithium ions in the cell is reduced by about 30%. Compared with the reference graph of FIG. 5, it can be seen that the minimum value portion of the cell voltage 600 has shifted from right to left. The PE voltage 610 does not change much, but the NE voltage 620 has shifted to the left compared to the reference NE voltage 520. The minimum value of the cell voltage 600 is located at a value greater than 0% of the state of charge (SOC). As the change of the state of charge (SOC) changes between 30% and 100% as the cell deteriorates, the capacity of the reference graph is reduced by about 30%. This is because electrolyte charge co-intercalation (SEI) and Li plating are caused by the loss of 30% of lithium ions to be charged and discharged.

FIG. 7 and FIG. 8 are graphs showing cell voltages and the like that appear in the case of the anode active material loss of the battery. FIG. 7 is a graph at charging and FIG. 8 is a graph at discharge.

Referring to FIG. 7, when the anode active material is reduced by about 30%, it can be seen that the cell voltage 700 of the charging graph has shifted from the left to the right in the minimum value region. That is, the minimum value of the cell voltage 700 during charging is not located at 0% of the state of charge (SOC) but moves to the left of the graph as the anode active material decreases. The PE voltage 710 did not change much, but the NE voltage 720 shifted to the right compared to the reference graph. As the right side of the change range of the state of charge (SOC) decreases, the capacity decreases by about 30%. This is because the anode active material is lost, cracking of the active material particles, electrical insulation / contact loss, and resistance film formation occur.

Referring to FIG. 8, the maximum value of the cell voltage 800 of the discharge graph when the anode active material is reduced by about 30% is slightly lower than the reference cell voltage. Also, the maximum value of the cell voltage is not located at 100% of the state of charge (SOC) but moves to the right of the graph as the anode active material decreases. The PE voltage 810 does not change much, but the NE voltage 820 moves to the right compared to the reference NE voltage 520. As the maximum position of the state of charge (SOC) shifts to the right, the change range of the state of charge (SOC) is about 0 ~ 88%, and the capacity of the reference graph is reduced by about 12%. This is because, as shown in FIG. 7, the anode active material is lost, cracking of the active material particles, electrical insulation / contact loss, resistance film formation, and the like occur.

FIG. 9 and FIG. 10 are graphs showing cell voltages and the like in the case of the cathode active material loss of the battery. Figure 9 is a graph at discharge and Figure 10 is graph at charge.

Referring to FIG. 9, when the cathode active material is reduced by about 30%, it can be seen that the cell voltage 900 of the discharge graph moves from the left to the right in the maximum value region. The maximum value of the cell voltage 900 at the time of discharging is not located at 100% of the state of charge SOC but at about 70% of the state of charge SOC. Therefore, the change range of the state of charge (SOC) ranges from 0 to 70%, and the capacity is reduced by about 30%. NE voltage 920 does not change, but the maximum value portion of PE voltage 910 shifts from left to right.

Referring to FIG. 10, when the cathode active material is reduced by about 30%, it can be seen that the cell voltage 1000 of the charge graph shifts from the right to the left of the minimum value region. The minimum value of the cell voltage 1000 during charging is not located at 0% of the state of charge (SOC) but is located at approximately 24% of its higher state of charge (SOC). Since the change range of the state of charge (SOC) is 24 to 100%, the capacity is reduced by approximately 24%. NE voltage 1020 does not change, but the minimum value portion of PE voltage 1010 moves from right to left.

11 is a diagram illustrating an example of a method for predicting battery life based on an average usage period of each group according to an embodiment of the present invention.

Referring to FIG. 11, a plurality of groups 1100, 1102, and 1104 are defined in advance according to the cause of battery deterioration. For example, as shown in FIGS. 6 to 10, it can be defined as three groups of lithium ion loss, anode active material loss, and cathode active material loss.

The battery life predicting device calculates the average usage periods 1110, 1112, and 1114 of the cells for each group. The battery life predicting device can calculate the average usage period (1100, 1102, 1104) for each group by considering the information about the previously accumulated cell and the information about the currently received cell. According to the embodiment, if there is a lot of information on the previously accumulated cells, only the information in a certain period (for example, recent 5 years) in the latest order or only a certain number The average period of use can be calculated.

The battery life predicting device calculates a value 1120 which is averaged again for the average usage periods 1110, 1112, and 1114 for each group. For example, among the cells of the same type of battery, A cells belong to the first group, B cells belong to the second group, and C cells belong to the Nth group. The battery life predicting device can calculate a weighted average in consideration of the number of cells belonging to each group with respect to the total number of cells when the average usage periods 1110, 1112, and 1114 for each group are averaged, have.

The battery life predicting device can estimate the remaining battery life according to the 1% reduction in the state of charge (SOC) by dividing the predefined state of charge (SOC) reference range 1130 by the average use period 1120. [ For example, if the SOC reference range is 80% and the average usage period is 9.1, the battery life predicting device estimates approximately 166 (9.1 years * 365 days / 20) .

12 is a diagram illustrating an example of a method of analyzing a correlation between a group of causes of battery deterioration and a battery use environment according to an embodiment of the present invention.

Referring to FIG. 12, a plurality of groups 1200, 1202 and 1204 can be defined according to the cause of battery deterioration. The battery life predicting device can receive information on the environment of the BMS such as the charge amount 1210, charge / discharge speed 1212, and internal / external temperature 91214 from the BMS. The information on the use environment shown in this embodiment is merely an example, and may be variously modified according to the embodiment, such as excluding some of the information shown in FIG. 12 or further including other information.

The battery life predicting device analyzes (1220) the correlation between the two groups based on the information (1210, 1212, 1214) about the usage environment of each group (1200, 1202, 1204) and the cell. Various methods for analyzing the conventional correlation can be applied to this embodiment. For example, as a result of the correlation analysis, the cause of deterioration of the first group 1200 has a correlation with the charge rate 1212, the cause of deterioration of the second group 1202 is the charge amount (e.g., overcharge) 1210 ). In this case, if the battery life predicting device classifies the battery into the first group 1200 based on the change in the cell voltage of the currently used battery, the battery life predicting device controls the charging speed through the BMS 1230 of the battery, You can slow down and extend battery life.

13 is a diagram illustrating an example of a configuration of a battery life predicting apparatus according to an embodiment of the present invention.

13, the battery life predicting apparatus 310 includes a setting unit 1300, a storage unit 1310, a grouping unit 1320, a prediction analyzing unit 1330, a correlation analyzing unit 1340, a controller 1350 And an information providing unit 1360.

The setting unit 1300 sets and updates the initial predicted life span of the battery. For example, the setting unit 1300 receives and registers an initial predicted life span of a new battery from a user when the battery is a new type of battery. Thereafter, the setting unit 1300 automatically updates the initial predicted lifetime through a method such as a screening method in FIG.

The storage unit 1310 receives and accumulates cell voltages and the like at the time of charging or discharging at least one cell belonging to the battery from at least one BMS. In addition to the cell voltage, the storage unit 1310 may receive and accumulate information such as the charging state (SOC) of the charging amount, the charging speed, the inside and outside temperature of the cell, and the like from the BMS.

The group classifying unit 1320 assigns cells to any one of the groups on the basis of the cell voltage indicating a different type of change for each group classified on the basis of the deterioration cause of the cell. For example, the group classifying unit 1320 may classify cells into any one of a plurality of groups based on a change pattern of a maximum value portion or a minimum value portion of the cell voltage.

The prediction analyzing unit 1330 analyzes the relationship between the change range of the state of charge (SOC) of the cell and the usage period for each group, and when the change range of the state of charge of the cell becomes the predetermined reference range Calculate the average of the periods. The setting unit 1300 updates the initial predicted life span with the average value calculated by the prediction analyzing unit 1330. [

The correlation analyzing unit 1340 analyzes the correlation between the usage environment (charge amount, charge rate, temperature, etc.) of each group and the cell, and grasps the use environment related to the cause of deterioration of each group. The method of correlation analysis is shown in Fig.

The controller 1350 controls the use environment related to the cause of deterioration of the group to which the battery currently in use belongs so that the life of the battery can be extended. 12, when the batteries belong to the first group 1200 and the charge rate 1212 has a correlation with the cause of deterioration of the first group 1200, To control the charging rate of the BMS.

The information providing unit 1360 informs the user of the group to which the battery belongs so that the cause of the deterioration of the battery can be identified. Also, if the correlation is analyzed by the correlation analysis unit 1340, the information providing unit 1360 can inform the user of the usage environment that affects the cause of deterioration of the group to which the battery belongs.

The present invention can also be embodied as computer-readable codes on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. The computer-readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner.

The present invention has been described with reference to the preferred embodiments. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.

Claims (9)

Setting an initial expected lifetime of the battery;
Receiving and storing a cell voltage during charging or discharging of at least one cell belonging to the battery;
Allocating cells to any one group based on a change in the cell voltage appearing in a plurality of groups defined on the basis of a deterioration cause of the cell including reduction in lithium ion, decrease in anode active material, or decrease in cathode active material;
Determining a use time until the change range of the charged state of the cell becomes the preset reference range based on the relationship between the change range of the state of charge (SOC) indicated by the group to which the cell belongs and the use period; And
And updating the initial predicted lifetime to a value obtained by averaging the usage periods of the plurality of cells for each group,
Wherein the battery life span is defined as the battery life span until the change range of the charge state becomes the reference range. The method according to claim 1,
And predicting a remaining lifetime in accordance with a variation range of the charged state of the battery based on the ratio of the initial predicted lifetime to the reference range. 2. The method of claim 1,
Allocating a cell to any one of a first group of lithium ion reduction, a second group of anode active material reduction, and a third group of cathode active material reduction based on a change form of a maximum value portion or a minimum value portion of the cell voltage And estimating the battery life. The method of claim 3,
And notifying a user of a deterioration cause predefined for each group to which the cell belongs. The method according to claim 1,
Analyzing a charge-discharge rate, a charge-discharge rate, or a temperature of the battery and a correlation between the respective groups; And
And controlling the charge discharge amount, the charge / discharge speed or the temperature according to the correlation. A setting unit for setting an initial predicted life span of the battery;
A storage unit for receiving and accumulating a cell voltage during charging or discharging of at least one cell belonging to the battery;
A group classification unit for assigning cells to any one group based on a change form of a cell voltage appearing in a plurality of groups defined on the basis of a cause of deterioration of a cell including reduction in lithium ion, decrease in anode active material, ; And
A prediction analyzing unit that grasps the usage period until the change range of the charged state of the cell becomes the predetermined reference range based on the relationship between the change range of the state of charge (SOC) indicated by the group to which the cell belongs and the use period Including,
Wherein the setting unit renews the initial predicted life span by a value obtained by averaging the use period per group for a plurality of cells,
Wherein the battery life predicting means is referred to as a battery life time until the change range of the charged state becomes the reference range. The method according to claim 6,
A correlation analyzer for analyzing the charge / discharge rate or temperature of the battery and the correlation between the groups; And
And controlling the charge / discharge amount, charge / discharge speed, or temperature according to the correlation. The method according to claim 6,
And notifying the user of the deterioration cause according to the group to which the cell belongs. A computer-readable recording medium storing a program for performing the method according to any one of claims 1 to 5.

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