KR20190073254A - Method and apparatus estimating a state of battery - Google Patents

Abstract

Disclosed are a method for estimating a battery state and an apparatus thereof. According to one embodiment of the present invention, the method for estimating a battery state decides state information of batteries in each of switching periods and outputs state information of each of the batteries in the last switching period of the switching periods. The apparatus for estimating a battery state sets a group of battery cells having similar performance or characteristics without limitation of a physical structure, thereby estimating a state of each of the battery cells in the corresponding group.

Description

[0001] METHOD AND APPARATUS ESTIMATING A STATE OF BATTERY [0002]

The following embodiments relate to battery state estimation.

There are various methods for estimating the state of the battery. In one example, the state of the battery may be estimated by integrating the current of the battery or estimated using a battery model (e.g., an electrical circuit model, or an electrochemical model).

A method for estimating a battery condition according to one side includes the steps of: determining status information of a plurality of batteries in each of a plurality of switching periods, the determining step comprising: Determining based on sensing data and the battery model of the target battery and determining the state information of the non-target battery based on a state change amount of the non-target battery; And outputting status information of each of the batteries in the last switching period of the switching periods.

Wherein the target battery set in the first switching period of the switching periods is set as a non-target battery in the second switching period of the switching periods, and the non-target battery in the first switching period is set as the target battery in the second switching period do.

The battery state estimation method may further include updating a relation set for the batteries based on at least one of state information of each of the batteries in the last switching period and a voltage magnitude of each of the batteries .

The set relation may indicate a sequence indicating which battery is the target battery in each switching period or groups formed by grouping the batteries.

The battery state estimation method may further include grouping the subset of the batteries into groups and allocating each of the battery models to each of the groups.

Wherein the determining of the status information of the target battery comprises determining status information of a corresponding target battery of each group in each switching period on the basis of a battery model assigned to each group and sensing data of a corresponding target battery of each group .

The number of members of each of the groups may be the same or different, and the number of the groups may be less than the number of the batteries.

The battery state estimation method may further include regrouping the subsets of the batteries when state information of each of the batteries in the last switching period is determined and a group update event occurs.

The group update event may occur based on at least one of a predetermined period, a travel distance of the vehicle using the batteries as a power source, and the determined status information.

The grouping and assigning may include grouping subsets of the batteries based on at least one of the voltage magnitude and the previous state information of each of the batteries.

The previous state information may include at least one of charge state information, life state information, and abnormality state information of each of the batteries determined before the first switching period of the switching periods.

The determining of the state information of the non-target battery may include determining the state information of the non-target battery by adding the state change amount to the previous state information of the non-target battery.

Each of the batteries may be battery cells, battery modules, or battery packs.

Determining the status information of the target battery may include determining status information of another target battery in each of the plurality of different switching periods of the switching periods.

The other target battery may be a non-target battery in a switching period other than the corresponding one of the other switching periods.

The battery model may be an electrochemical model.

The battery condition estimating apparatus according to claim 1, wherein the battery state estimating apparatus according to one side determines state information of the batteries in each of switching periods, wherein, for each switching period, state information of the target battery is calculated based on the sensing data of the target battery and the battery model Target battery state information based on the state change amount of the non-target battery, and outputs the state information of each of the batteries in the last switching period of the switching periods.

Wherein the target battery set in the first switching period of the switching periods is set as a non-target battery in the second switching period of the plurality of switching periods, and the non- .

The processor may update the relationship established for the batteries based on at least one of the state information of each of the batteries in the last switching period and the voltage magnitude of each of the batteries.

The set relation may indicate a sequence indicating which battery is the target battery in each switching period or groups formed by grouping the batteries.

The processor may group the subsets of the batteries into groups and assign each of the battery models to each of the groups.

The processor may determine status information of a corresponding target battery of each group in each switching period on the basis of a battery model allocated to each group and sensing data of a corresponding target battery of each group.

The number of members of each of the groups may be the same or different, and the number of the groups may be less than the number of the batteries.

The processor may re-group subsets of the batteries when state information of each of the batteries in the last switching period is determined and a group update event occurs.

The group update event may occur based on at least one of a predetermined period, a travel distance of the vehicle using the batteries as a power source, and the determined status information.

The processor may group the subsets of the batteries based on at least one of the voltage magnitude and the previous state information of each of the batteries.

The previous state information may include at least one of charge state information, life state information, and abnormality state information of each of the batteries determined before the first switching period of the switching periods.

The processor may determine the state information of the non-target battery by adding the state change amount to the previous state information of the non-target battery.

The battery model may be an electrochemical model.

1 is a view for explaining a battery system according to an embodiment.
FIGS. 2 to 3 are diagrams for explaining a method of determining a state information of each of the batteries in a switching period according to an embodiment of the present invention.
4 is a diagram for explaining that the battery state estimation apparatus according to the embodiment updates the sequence of the batteries.
5 to 8 are views for explaining the grouping of the batteries by the battery state estimation apparatus according to an embodiment.
FIGS. 9A and 10B are diagrams for explaining a process of determining state information of each of the batteries in a group according to an embodiment of the present invention.
11 is a view for explaining a group update of the battery state estimation apparatus according to an embodiment.
12 is a block diagram for explaining a battery state estimation apparatus according to an embodiment.
13 is a flowchart illustrating a battery state estimation method according to an embodiment.
14 is a view for explaining a vehicle according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

Various modifications may be made to the embodiments described below. It is to be understood that the embodiments described below are not intended to limit the embodiments, but include all modifications, equivalents, and alternatives to them.

The terms used in the examples are used only to illustrate specific examples, and are not intended to be limiting. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises" or "having" and the like refer to the presence of stated features, integers, steps, operations, elements, components, or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this embodiment belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as ideal or overly formal in the sense of the art unless explicitly defined herein Do not.

In the following description of the present invention with reference to the accompanying drawings, the same components are denoted by the same reference numerals regardless of the reference numerals, and redundant explanations thereof will be omitted. A detailed description of related arts will be omitted if it is determined that a detailed description of the related art may unnecessarily hinder understanding of the structure or operation of the embodiment.

1 is a view for explaining a battery system according to an embodiment.

1, a battery system 100 according to an embodiment includes batteries 110-1 to 110-n and a battery state estimation device 120. [

Each of the batteries 110-1 to 110-n may be a battery cell, a battery module, or a battery pack.

The battery state estimation apparatus 120 senses each of the batteries 110-1 to 110-n using one or more sensors. In other words, the battery state estimation device 120 collects sensing data of each of the batteries 110-1 to 110-n. The sensing data may include, for example, any one of voltage data, current data, and temperature data, or a combination thereof. The sensing data is not limited to the above examples.

The battery state estimation apparatus 120 sets a relation to the batteries 110-1 to 110-n. The relationship may indicate, for example, the order in which each battery becomes the target battery in each of the switching periods or the groups formed by grouping the batteries 110-1 to 110-n. For example, the battery condition estimating apparatus 120 estimates the order of the batteries 110-1 to 110-n based on at least one of the voltage magnitude and the previous state information of each of the batteries 110-1 to 110- Or may group the batteries 110-1 to 110-n into groups. The previous state information includes charge state information (e.g., state of charge (SOC)) of each of the batteries 110-1 to 110-n determined before the first switching period of the switching periods, life state information , State of health (SOH)), and abnormality status information. The order and grouping will be described later.

The battery state estimation apparatus 120 determines the state information of each of the batteries 110-1 to 110-n in each switching period when a relationship to the batteries 110-1 to 110-n is set. For example, the battery state estimation device 120 can determine the state information of the target battery in the individual switching period by using the sensing data of the battery model and the target battery, and determine the state information of the non-target battery in the individual switching period It can be determined based on the state information in the previous switching period. The details will be described later with reference to FIG. 2 to FIG. When the batteries 110-1 to 110-n are divided into groups, the battery state estimation device 120 estimates the state information of the target battery belonging to each group in the individual switching period, The state information of the non-target battery belonging to each group can be determined based on the state information in the previous switching period. Details will be described later with reference to Figs. 9A to 10B.

The battery condition estimating apparatus 120 estimates the battery condition based on at least one of the state information of each of the batteries 110-1 to 110-n in the last switching period and the voltage magnitude of each of the batteries 110-1 to 110-n The relationship set for the batteries 110-1 to 110-n can be updated. This update may change the order of the batteries 110-1 to 110-n. Sequence update will be described later with reference to FIG. Also, at least one of the members of each group, the number of members of each group, and the number of groups may be changed by the update. Group updating will be described later with reference to FIGS. 11A to 11C.

FIGS. 2 to 3 are diagrams for explaining a method of determining the state information of each of the batteries in the switching period according to an embodiment of the present invention.

Referring to FIG. 2, the battery state estimation apparatus 120 determines state information of each of the batteries 110-1 to 110-n in each of a plurality of switching periods (210). More specifically, the battery state estimation device 120 determines state information of the target battery in each switching period on the basis of the sensing data and the battery model of the target battery in each switching period. The procedure to be described later determines which battery corresponds to the target battery in each switching period. The battery model may be, for example, an electrical circuit model or an electrochemical model. The battery model is not limited to the above example. The battery state estimation device 120 determines the state information of the non-target battery in each switching period based on the state change amount in each switching period.

In the example shown in FIG. 2, it is assumed that the order of the batteries 1 to 96 is Battery 1, Battery 2, ..., Battery 96 during the Nth order update period. The order of the batteries 1 to 96 may indicate the order in which each battery becomes the target battery in the Nth order update period. Since Battery 1 is the first order in the Nth order updating period, it becomes the first target battery among the batteries 1 to 96. In other words, the battery 1 corresponds to the target battery in the first switching period T1. Battery 96 is the last target battery in the N-th order update period since it is the last order. In other words, the battery 96 corresponds to the target battery in the last switching period T96.

The order of the batteries 1 to 96 is periodically updated, which may mean a period in which the order of the batteries 1 to 96 is updated.

The battery state estimation device 120 can determine the state information of the target battery (i.e., the battery 1) in the first switching period T1 based on the sensing data of the battery 1 and the battery model. For example, the battery state estimation apparatus 120 can extract sensing data corresponding to the switching period T1 from the sensing data of the battery 1, and input the sensed data to the battery model. The battery model can derive the state information of the battery 1 based on the received sensing data.

The battery state estimation device 120 can determine the state information of each of the non-target batteries (i.e., the battery 2 to the battery 96) in the switching period T1 based on the state change amount? ₁ . ₁ will be described later. For example, the battery state estimation apparatus 120 adds the state change amount? ₁ to the state information of each of the batteries 2 to 96 in the (N-1) Can be determined.

Table 1 below shows an example of status information of each of the batteries 1 to 96 in the switching period T1.

battery Status information Battery 1 α ₁ Battery 2 SOC _N-1 # 2 + DELTA ₁ Battery 3 SOC _N-1 # 3 +? ₁ Battery 4 SOC _N-1 # 4 +? ₁ ... ... Battery 95 SOC _N-1 # 95 +? ₁ Battery 96 SOC _N-1 # 96 +? ₁

In Table 1 above, α ₁ indicates the SOC of the battery 1 at the switching period T1 to be determined by the battery model, _{SOC N-1 # 2, SOC} N-1 # 3, ..., _N-1 # and the SOC 96 each represent the SOC of each of the batteries 2 to 96 in the (N-1) -th sequential update period.

In one embodiment, < RTI ID = 0.0 > ₁ may < / RTI > The battery state estimation apparatus 120 may calculate? ₁ based on the amount of current and the reference capacity flowing during the switching period T1 in the battery pack including the battery 1 to the battery 96. [ For example, the battery state estimation apparatus 120 can calculate? ₁ according to Equation (1) below.

In Equation (1), t ₁ represents the start time of the switching period T ₁ , t ₂ represents the end time of the switching period T 1, and reference capacity represents a total capacity of the battery of the same type as that of the batteries 1 to 96. I represents the current flowing in the battery pack.

In the example shown in Fig. 2, the target battery is switched or changed from the battery 1 to the battery 2 in the switching period T2 since the battery 2 is in the second order (i.e., the order in which the target battery becomes the second target). In other words, the battery state estimation device 120 can select the battery 2 as the target battery in the switching period T2 in consideration of the order. In the switching period T2, the battery 1 and the batteries 3 to 96 correspond to non-target batteries.

The battery state estimation device 120 can determine the state information of the target battery (i.e., the battery 2) in the switching period T2 based on the sensing data of the battery 2 and the battery model. For example, the battery state estimation apparatus 120 may extract sensing data corresponding to the switching period T2 from the sensing data of the battery 2, and may input the sensed data to the battery model. The battery model can derive state information of the battery 2 in the switching period T2 from the sensing data input thereto. At this time, the battery state estimation apparatus 120 or the battery model can correct the state information of the battery 2.

3, the target battery is switched from the battery 1 to the battery 2 in the switching period T2 so that the battery model inputs the sensing data 320 of the battery 2 rather than the sensing data 310 of the battery 1 . In other words, there may be a discontinuity between the input data 310 and the input data 320 of the battery model at the switching time. When the battery model derives and outputs the state information of the battery 2 from the input data 320 in the state where the discontinuity exists, the output of the battery model is discontinuous between T1 and T2 as shown in the graph 330. [ According to an embodiment, the battery state estimation apparatus 120 may correct the output of the battery model in each switching period using a separately constructed calibration model or filter (e.g., a Kalman filter). As such, the calibrated output may be continuous, such as graph 340. Depending on the implementation, the battery model has a built-in calibration function so that the output of the battery model can be continuous, such as graph 340.

2, the battery state estimation apparatus 120 can determine the state information of each of the non-target batteries in the switching period T2 (i.e., the battery 1 and the battery 3 to the battery 96) based on the state change amount? ₂ . For example, the battery state estimation apparatus 120 adds the state change amount? ₂ to the state information of each of the battery 1 and the battery 3 to the battery 96 in the switching period T1 to determine the state of the battery 1 and the battery 3 to the battery 96 in the switching period T2 State information can be determined. Since the description of the prior △ ₁ can be applied to the △ _2, △ and detailed description thereof will not be given for the _second.

Table 2 below shows an example of status information of each of the batteries 1 to 96 in the switching period T2.

battery Status information Battery 1 α ₁ + Δ ₂ Battery 2 α ₂ Battery 3 SOC _N-1 # 3 +? ₁ +? ₂ Battery 4 SOC _N-1 # 4 +? ₁ +? ₂ ... ... Battery 95 SOC _N-1 # 95 +? ₁ +? ₂ Battery 96 SOC _N-1 # 96 +? ₁ +? ₂

In Table 2 above,? ₂ is determined by the battery model and represents the SOC of Battery 2 in the switching period T2.

For each of the switching periods T3 to T96, the battery state estimation apparatus 120 can operate in accordance with the above-described method.

Table 3 below shows an example of status information of each of the batteries 1 to 96 in the last switching period T96.

battery Status information Battery 1 ? ₁ +? ₂ + ... +? ₉₆ Battery 2 ? _{2 +} ? ₃ + ... +? ₉₆ Battery 3 ? ₃₊ ? ₄ + ... +? ₉₆ Battery 4 ? ₄₊ ? ₅ + ... +? ₉₆ ... ... Battery 95 α _{95 +} Δ ₉₆ Battery 96 α ₉₆

The battery state estimation device 120 can operate as described above to determine the state information of each of the batteries 1 to 96 quickly and accurately.

For convenience of description, state information of each of the batteries 1 to 96 in Table 3 is expressed as shown in Table 4 below.

SOC _N # 1 =? ₁ +? ₂ + ... +? ₉₆ SOC _N # 2 =? _{2 +} ? ₃ + ... +? ₉₆ SOC _N # 3 =? ₃₊ ? ₄ + ... +? ₉₆ SOC _N # 4 =? ₄₊ ? ₅ + ... +? ₉₆ ... _{SOC N # 95 = α 95+ △} 96 SOC _N # 96 = α ₉₆

4 is a diagram for explaining that the battery state estimation apparatus according to the embodiment updates the sequence of the batteries.

Referring to FIG. 4, the battery state estimation apparatus 120 estimates the battery 110-1 to 110-n based on state information of each of the batteries 110-1 to 110-n in the last switching period of a given order update period, (step 410). For example, the battery state estimation device 120 may update or determine the order of the batteries 1 to 96 in the order of the largest or smallest among the SOC _N # 1 to SOC _N # 96. In other words, the battery state estimation device 120 can sort the SOC _N # 1 to SOC _N # 96 in ascending or descending order and update or determine the order of the batteries 1 to 96 based on the result of the sorting . In another example, the battery state estimation device 120 can update or determine the order of the batteries 1 to 96 in the order of the voltages of the batteries 1 to 96 being large (or small). In the example shown in Fig. 4, the descending order of SOC _N # 1 to SOC _N # 96 is SOC _N # 41 (maximum), SOC _N # 22, SOC _N # 33, SOC _N # _N # 25 and SOC _N # 66 (minimum), the battery state estimation device 120 sets the order of the batteries 1 to 96 to 41, 22, 33, 14, ..., 26, 66 Update or decide.

Based on the updated order, the battery state estimation apparatus 120 can determine the state information of each of the batteries 1 to 96 in each of the switching periods T97 to T192 (420). The battery state estimation apparatus 120 may repeat the operations described with reference to FIGS. 2 to 3 after the (N + 1) -th update period.

5 to 8 are views for explaining the grouping of the batteries by the battery state estimation apparatus according to an embodiment.

According to one embodiment, the battery state estimation apparatus 120 may include a plurality of battery models, and groups the batteries 110-1 to 110-n to be allocated to the corresponding battery model. For example, when the battery pack 500 includes the battery modules 1 to 4 and each of the battery modules 1 to 4 includes a plurality of battery cells, The groups may be formed by performing unit grouping or cell-by-cell grouping, and a battery model may be assigned to each group. The module-by-module grouping will be described with reference to FIGS. 6A to 6D, and the cell-by-cell grouping will be described with reference to FIGS. 7A to 7C.

In an embodiment, the battery condition estimation device 120 may include the same number of battery models as the number of battery modules. In other words, the number of battery models in the battery state estimation device 120 may be equal to the number of battery modules. In this embodiment, the battery condition estimation device 120 may configure the battery cells belonging to each battery module as one group. In the example shown in FIG. 6A, the number of battery models is four and the number of battery modules is four. The battery state estimation device 120 can set or configure the battery cells (or the battery module 1) belonging to the battery module 1 to the group 1 and allocate the battery model 1 to the group 1, (Or battery module 2) can be set or configured as group 2 and battery model 2 assigned to group 2. In addition, the battery state estimation device 120 can set or configure the battery cells (or the battery module 3) belonging to the battery module 3 to the group 3 and allocate the battery model 3 to the group 3, Battery cells (or battery module 4) can be set or configured as group 4 and battery model 4 assigned to group 4.

In another embodiment, the battery condition estimation device 120 may include a different number of battery models than the number of battery modules. In other words, the number of battery models in the battery state estimation device 120 may be different from the number of battery modules.

For example, the number of battery models may be less than the number of battery modules. In this case, the battery state estimation device 120 may form the battery cells belonging to the other battery modules into one group. In the example shown in FIG. 6B, the number of battery modules is four and the number of battery models is three. In this case, the battery state estimation device 120 can set the battery cells belonging to the battery module 1 and the battery cells (or the battery modules 1 and 2) belonging to the battery module 2 to the group 1 and assign the battery model 1 to the group 1 . The battery state estimation device 120 can set the battery cells (or the battery module 3) belonging to the battery module 3 to the group 2, allocate the battery model 2 to the group 2, Battery module 4) can be set to group 3 and battery model 3 can be assigned to group 3. In the example shown in FIG. 6C, the number of battery modules is four and the number of battery models is two. In this case, the battery state estimation device 120 can form the battery cells belonging to each of the battery module 1 and the battery module 2 as a group 1, and the battery cells belonging to each of the battery module 3 and the battery module 4 as a group 2 . The battery state estimation apparatus 120 can assign the battery model 1 to the group 1 and the battery model 2 to the group 2.

In another example, the number of battery models in the battery state estimation device 120 may be greater than the number of battery modules. In this example, the battery condition estimation device 120 can select at least one battery module among the battery modules, and can assign two or more battery models to the selected battery module. For example, the battery state estimation device 120 may select at least one battery module or arbitrarily select at least one battery module in consideration of SOC, SOH, or abnormal state of each battery module. The battery state estimation unit 120 may allocate two or more battery models to at least one selected battery module. In the example shown in FIG. 6D, the number of battery modules is four and the number of battery models is five. In this case, the battery state estimation device 120 can select the battery module 2 having the highest SOC, and can assign the battery models 2 and 3 to the selected battery module 2.

In one embodiment, the battery state estimation apparatus 120 includes N battery models and the battery pack 500 includes M battery cells. Here, M and N are integers, and M and N may be the same or different from each other. In this embodiment, the battery condition estimation device 120 may divide the battery cells into N groups. At this time, the number of members in each group may be the same or different.

First, the case where the number of members in each group is the same will be described. The battery state estimation apparatus 120 may group the battery cells such that the number of members of each of the N groups is the same. In other words, the number of battery cells belonging to each group may be equal to M / N. In the example shown in FIG. 7A, the number of battery models is 4 and the number of battery cells is 40. In this case, the battery state estimation apparatus 120 divides 40 battery cells into four groups so that the number of members of each group is equal to ten. The battery state estimation apparatus 120 may allocate battery models 1 to 4 to each of the four groups.

The battery state estimation device 120 can group battery cells with different numbers of members in each group. In one example, the battery condition estimation device 120 may divide the battery cells into N groups such that a particular group includes fewer battery cells. In the example shown in FIG. 7B, the battery state estimation apparatus 120 forms seven battery cells, which can form eight battery cells whose SOCs are determined to be relatively high as group 1, and whose SOCs are relatively low, as group 4 . The battery state estimation apparatus 120 can form fifteen battery cells having the highest SOC among the remaining twenty five battery cells as the group 2 and the remaining ten battery cells as the group 3. Here, the relative high and low determination of the SOC may be performed based on a predetermined high or low threshold. Intermediate predetermined levels can also be determined. The battery state estimation apparatus 120 may allocate battery models 1 to 4 to each of the four groups. The number of members in each group is only an example, and the number of members in each group is not limited to the above example. 7B, the number of members in group 1 equals the number of members in group 4, the number of members in group 2 equals the number of members in group 3, the number of members in groups 1 and 4 is equal to the number of members in group 2 And 3, respectively.

The number of members of group 1 and / or group 4 described with reference to FIG. 7B may be one. For example, the battery state estimation apparatus 120 can form a group 1 having only a battery cell having a maximum SOC as a member and a group 4 having only a battery cell having a minimum SOC, Group 2 and group 3, respectively.

If the number of battery models is three in the example shown in FIG. 7B, the battery state estimation apparatus 120 can form 10 battery cells having a high SOC as a group 1 and 10 battery cells having a low SOC as a group 3 And the remaining 20 battery cells may be formed as a group 2.

In another embodiment, the battery state estimation apparatus 120 may divide battery cells into N or less groups. 7A and 7B, in the case of the example shown in FIG. 7C, the battery state estimation apparatus 120 including four battery models can divide 40 battery cells into three groups instead of four groups . For example, the number of members of group 1 may be 10, the number of members of group 2 may be 20, and the number of members of group 3 may be 10, as in the example shown in FIG. 7C. The number of members in each group is only an example, and the number of members in each group is not limited to the above example. In this case, the battery state estimation apparatus 120 can select a group considering at least one of SOC, SOH, and abnormal state among the groups 1 to 3, and can assign two or more battery models to the selected group. The battery state estimation apparatus 120 can assign the battery models 1 and 4 to the group 1 when the group 1 having the highest SOC is selected. The battery state estimation apparatus 120 can assign the battery model 2 to the group 2 and assign the battery model 3 to the group 3.

In the case of cell-by-cell grouping, the battery cells can be grouped without being restricted to the physical structure. In other words, the battery cells belonging to the same battery module can belong to different groups, and the battery cells belonging to different battery modules can belong to the same group. The battery state estimating apparatus 120 may estimate the state of each of the battery cells in the group by setting the battery cells having similar performance or characteristics as the group without limitation to the physical structure. As a result, the estimation accuracy of the state of each of the battery cells in the group can be improved.

The battery pack 500 described with reference to FIG. 5 includes a plurality of battery modules in which a plurality of battery cells are modularized. As shown in FIG. 8, a plurality of battery cells may be formed as one battery pack 800. In this case, the battery state estimation apparatus 120 may group the battery cells in the battery pack 800 on a cell-by-cell basis.

FIGS. 9A and 10B are diagrams for explaining a process of determining state information of each of the batteries in a group according to an embodiment of the present invention.

In the example described with reference to FIGS. 9A to 9C, battery cells 1 to 10 are formed or assigned to group 1, and battery cells 11 to 20 are formed or assigned to group 2. Further, the battery cells 21 to 30 are formed or assigned to the group 3, and the battery cells 31 to 40 are formed or assigned to the group 4. Groups 1 to 4 can be formed by the battery state estimation device 120 performing the above-described grouping of module units or the above-described cell-by-cell grouping. The battery state estimation device 120 allocates each of the battery models 1 to 4 to each of the groups 1 to 4. The battery state estimation device 120 performs the operations described with reference to Figs. 2 to 4 for each of the groups 1 to 4. In other words, the battery state estimation apparatus 120 can determine the state information of each of the battery cells belonging to each group by performing the operations described with reference to Figs. 2 to 4 for each group. 9A to 9C show the operation result of the battery condition estimating apparatus 120 performed for each group.

9A to 9C, the hatched area indicates a battery cell in which the battery model is used to determine the state information. In other words, the hatched areas in Figs. 9A to 9C represent target batteries in each switching period. For example, in the switching period T5, the regions of the battery 5 of the group 1, the battery 15 of the group 2, the battery 25 of the group 3, and the battery 35 of the group 4 are indicated by shading. The hatched in the switching period T5 means that the state information of each of the battery 5, the battery 15, the battery 25, and the battery 35 in the switching period T5 is determined by the battery model assigned to each group. In other words, in the switching period T5, the target battery of group 1 is battery cell 5, the target battery of group 2 is battery cell 15, the target battery of group 3 is battery cell 25, and the target battery of group 4 is battery cell 35 .

Table 5 below shows an example of state information of each of the battery cells 1 to 40 in the last switching period T37.

group Battery number Status information Group 1 Battery 1 α ₃₇ Battery 2 ? ₃₆ +? ₃₇ ... ... Battery 10 ? _{28 +} ? ₂₉ + ... +? ₃₇ Group 2 Battery 11 β ₃₇ Battery 12 beta ₃₆ +? ₃₇ ... ... Battery 20 ? _{28 +} ? ₂₉ + ... +? ₃₇ Group 3 Battery 21 γ ₃₇ Battery 22 ? ₃₆ +? ₃₇ ... ... Battery 30 ? _{28 +} ? ₂₉ + ... +? ₃₇ Group 4 Battery 31 隆₃₇ Battery 32 δ ₃₆ + △ ₃₇ ... ... Battery 40 ? _{28 +} ? ₂₉ + ... +? ₃₇

In the above Table 5,? _T is determined by the battery model 1 allocated to the group 1 in the switching period as the state information of the target battery of the group 1 in the switching period, and? _T is the capacity of the group 2 target battery State information, which is determined by battery model 2 assigned to group 2. Further, from Table 5 above, γ _T is determined by the group 3, the battery model 3 is assigned to Group 3 in the switching period to the state information of the target battery of the switching period, δ _T is the target of a group of four of the switching period, Battery status information is determined by battery model 4 assigned to group 4. In the above-mentioned α _T , β _T , γ _T , and δ _T , T represents the switching period.

1 through 8 can be applied to the matters described with reference to FIGS. 9A through 9C, detailed description thereof will be omitted.

10A to 10B, battery cells 1 to 10 are formed or assigned to group 1, battery cells 11 to 30 are formed or assigned to group 2, and battery cells 31 to 40 are formed into group 3 Or assigned. Groups 1 to 3 can be formed by the battery state estimation device 120 performing the above-described grouping of module units or the above-described cell-by-cell grouping. The battery state estimation device 120 allocates battery models 1 to 3 to each of the groups 1 to 3, respectively.

The number of members in each of group 1 and group 3 is smaller than the number of members in group 2. As a result, for each of the members of group 1 and group 3, the battery model is utilized and the number of times the state information is determined is greater than for the members of group 2. In other words, the total number of times that each member of group 1 and group 3 becomes the target battery is greater than the total number of times the member of group 2 becomes the target battery. For example, in the case of the battery cell 12 of the group 2, the number of times the state information is determined by utilizing the battery model 2 allocated to the group 2 is 1 in total. On the other hand, in the case of each of the battery cells 2 of the group 1 and the battery cells 32 of the group 3, the number of times the state information is determined by utilizing the battery model allocated to each group is four times in total. In other words, in FIGS. 10A to 10B, there are four hatched areas for each of the battery cell 2 and the battery cell 32, which is larger than that of the battery cell 12 of the group 2 described above. As a result, the state of each of the battery cell 2 and the battery cell 32 can be estimated more accurately.

In addition, the battery state estimation device 120 can prevent the batteries in the group with a small number of members from being overdischarged or overcharged by grouping the batteries 1 to 40 by reducing the number of members of some groups. For example, if the battery cells 1 to 40 are being charged, the battery state estimation unit 120 may determine the state information of each of the battery cells belonging to the group 1 having a relatively high SOC, using the battery model 1 assigned to the group 1, So that the charging can be controlled so that the battery cells belonging to the group 1 are not overcharged. If the battery cells 1 to 40 are being discharged, the battery state estimation unit 120 may check the status information of each of the battery cells belonging to the group 3, which is relatively low in SOC, more accurately and frequently using the battery model 3 allocated to the group 3 And discharge can be controlled so that battery cells belonging to group 3 are not overdischarged.

1 through 8 can be applied to the matters described with reference to FIGS. 10A through 10B, detailed description thereof will be omitted.

11A to 11C are diagrams for explaining a group update of the battery state estimation apparatus according to an embodiment.

When the group update event occurs in the current group update period, the battery state estimation unit 120 performs the group update. The group update may occur based on at least one of the travel distance of the vehicle and the SOH (or the degree of deterioration), for example, when a predetermined time has elapsed. Referring to the example shown in FIG. 11A, the length of the group update period may correspond to the first to Nth update periods. The Nth order update from the first time is performed during the first group update period. When the Nth order update period elapses, a group update event may occur. Referring to the example shown in FIG. 11B, the group update event may occur based on the mileage of the vehicle on which the battery state estimation apparatus 120 is mounted. For example, if the mileage of the vehicle is determined to be greater than a predetermined distance (e.g., 700 km), a group update event may occur. At this time, a group update event may occur even if the time corresponding to the length of the group update period described with reference to FIG. 11A has not elapsed. Referring to the example shown in FIG. 11C, the group update event may occur based on the SOH (or deterioration degree) of the battery pack 500 or 800. For example, if the SOH of the battery pack 500 or 800 is less than 0.91, a group update event may occur. At this time, a group update event may occur even if the time corresponding to the length of the group update period described with reference to FIG. 11A has not elapsed.

The group update may change one or both of the members belonging to each of the groups, the number of each member of the groups, and the number of groups. For example, during the first group update period, battery cells 1 to 10 belong to group 1, battery cells 11 to 20 belong to group 2, battery cells 21 to 30 belong to group 3, and battery cells 31 to 40 Let's say that it belongs to group 4. When the group update event occurs, the battery state estimation unit 120 may perform the above-described group-by-module grouping or the above-described cell-by-cell grouping to regroup the battery cells 1 to 40. [ The number of members and / or members of each group can be changed by group update. For example, nine battery cells may belong to group 1, 11 battery cells may belong to group 2, 13 battery cells may belong to group 3, and seven battery cells may belong to group 4 . Also, the number of groups can be changed by group update. For example, the number of groups may be reduced from four to less than three or more than five. In addition, even if there is such a group update, the members of each group may not be changed.

12 is a block diagram for explaining a battery state estimation apparatus according to an embodiment.

Referring to FIG. 12, the battery state estimation apparatus 120 includes a memory 1210 and a processor 1220.

Memory 1210 is coupled to processor 1220 and may store instructions executable by processor 1220. [ Memory 1220 may include non-volatile computer readable media, such as high speed random access memory and / or non-volatile computer readable storage media.

The processor 1220 may execute instructions for performing one or more operations as described with reference to Figs. 1 to 11C. In one example, the processor 1220 determines status information of the batteries 110-1 through 110-n in each of the switching periods. Here, the processor 1220 determines state information of the target battery in each switching period on the basis of the sensing data and the battery model of the target battery in each switching period, and transmits the state information of the non-target battery in each switching period to each switching Based on the amount of state change of the non-target battery in the period. The processor 1220 outputs status information of each of the batteries 110-1 to 110-n in the last switching period of the switching periods.

The processor 1220 may be configured to determine whether the batteries 110-1 to 110-n are in the last switching period based on at least one of the status information of each of the batteries 110-1 to 110-n and the voltage magnitude of each of the batteries 110-1 to 110- 110-1 through 110-n). The relationship represents, for example, the above-mentioned order or groups.

1 to 11C can be applied to the matters described with reference to FIG. 12, so that a detailed description will be omitted.

13 is a flowchart illustrating a battery state estimation method according to an embodiment.

The battery state estimation method according to one embodiment may be performed by the battery state estimation apparatus 120. [

Referring to FIG. 13, the battery state estimation apparatus 120 determines state information of the batteries 110-1 through 110-n in each of the switching periods (operation 1310). In step 1310, the battery state estimation apparatus 120 determines 1311 the state information of the target battery in each switching period based on the sensing data and the battery model of the target battery in each switching period, Target battery state information on the basis of the state change amount of the non-target battery in each switching period (1312).

The battery state estimation apparatus 120 outputs state information of each of the batteries 110-1 to 110-n in the last switching period of the switching periods (operation 1320).

1 to 11C can be applied to the matters described with reference to FIG. 13, detailed description will be omitted.

The battery condition estimating device 120 may be mounted on various electronic devices (for example, a vehicle, a walking aid, a drones, a mobile terminal) using batteries as a power source, Can be performed. Hereinafter, a case in which the battery state estimation device 120 is mounted on a vehicle will be described with reference to Fig. 14 can be applied to other electronic apparatuses.

14 is a view for explaining a vehicle according to an embodiment.

14, the vehicle 1400 includes a battery pack 500 or 800 and a battery management system (BMS) 1410. Vehicle 1400 may be, for example, an electric vehicle or a hybrid vehicle.

The battery management system 1410 may monitor whether an abnormality has occurred in the battery pack 500 or 800 so that the battery pack 500 or 800 may not be overcharged or over- . The battery management system 1410 may also control the battery pack 500 or 800 if the temperature of the battery pack 500 or 800 exceeds a first temperature (e.g., 40 占 폚) or less than a second temperature 800 < / RTI > In addition, the battery management system 1410 performs cell balancing so that the charging state between the battery cells included in the battery pack 500 or 800 can be equalized.

According to one embodiment, the battery management system 1410 may include a battery state estimation device 120 and may be configured to determine state information of each of the battery cells in the battery pack 500 or 800 through the battery state estimation device 120 You can decide. The battery management system 1410 can determine the maximum value, the minimum value, or the average value of the state information of each of the battery cells as the state information of the battery pack 500 or 800. [

The battery management system 1410 may transmit status information of the battery pack 500 or 800 to an electronic control unit (ECU) or a vehicle control unit (VCU) of the vehicle 1400. The ECU or VCU of the vehicle 1400 can output the status information of the battery pack 500 or 800 to the display of the vehicle 900. [

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

Although the embodiments have been described with reference to the drawings, various technical modifications and variations may be applied to those skilled in the art. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (24)

The method comprising the steps of: determining status information of a plurality of batteries in each of a plurality of switching periods, the determining comprising, for each switching period, Determining the state information of the non-target battery based on the amount of change in state of the non-target battery; And
Outputting state information of each of the batteries in the last switching period of the switching periods
Lt; / RTI >
Wherein the target battery set in the first switching period of the switching periods is set as a non-target battery in the second switching period of the switching periods, and the non-target battery in the first switching period is set as the target battery in the second switching period felled,
Battery state estimation method.
The method according to claim 1,
Updating a relationship established for the batteries based on at least one of the state information of each of the batteries in the last switching period and the voltage magnitude of each of the batteries
≪ / RTI >
Battery state estimation method.
3. The method of claim 2,
Wherein the set relation represents a sequence indicating which battery is a target battery in each switching period or groups formed by grouping the batteries,
Battery state estimation method.
The method according to claim 1,
Grouping the subsets of the batteries into groups and assigning each of the battery models to each of the groups
≪ / RTI >
Battery state estimation method.
5. The method of claim 4,
Wherein determining the state information of the target battery comprises:
Determining status information of a corresponding target battery of each group in each switching period based on sensing data of a battery model assigned to each group and a corresponding target battery of each group
/ RTI >
Battery state estimation method.
5. The method of claim 4,
The number of groups being equal to or less than the number of batteries,
Battery state estimation method.
5. The method of claim 4,
Grouping the subsets of the batteries when the status information of each of the batteries in the last switching period is determined and a group update event occurs,
Further comprising:
Wherein the group update event is generated based on at least one of a predetermined period, a travel distance of the vehicle using the batteries as a power source, and the determined state information.
Battery state estimation method.
5. The method of claim 4,
Wherein the grouping and assigning comprises:
Grouping the subsets of the batteries based on at least one of the voltage magnitude and the previous state information of each of the batteries
/ RTI >
Battery state estimation method.
9. The method of claim 8,
Wherein the previous state information includes at least one of charge state information, life state information, and abnormality state information of each of the batteries determined before the first switching period of the switching periods.
Battery state estimation method.
The method according to claim 1,
Determining the state information of the non-target battery comprises:
Determining the state information of the non-target battery by adding the state change amount to the previous state information of the non-target battery
/ RTI >
Battery state estimation method.
The method according to claim 1,
Each of the batteries may be battery cells, battery modules, or battery packs.
Battery state estimation method.
The method according to claim 1,
Wherein determining the state information of the target battery comprises:
Determining status information of another target battery in each of the plurality of different switching periods of the switching periods
Lt; / RTI >
Wherein the other target battery is a non-target battery in a switching period other than a corresponding one of the other switching periods.
Battery state estimation method.
The method according to claim 1,
The battery model is an electrochemical model,
Battery state estimation method.
Determining state information of the batteries in each of the switching periods, determining state information of the target battery based on the sensing data and the battery model of the target battery for each switching period, Target battery based on a state change amount of the non-target battery, and outputs state information of each of the batteries in the last switching period of the switching periods,
Lt; / RTI >
Wherein the target battery set in the first switching period of the switching periods is set as a non-target battery in the second switching period of the plurality of switching periods, and the non- Is set to
A battery state estimation device.
15. The method of claim 14,
Wherein the processor updates a relationship established for the batteries based on at least one of state information of each of the batteries in the last switching period and a voltage magnitude of each of the batteries.
A battery state estimation device.
16. The method of claim 15,
Wherein the set relation represents a sequence indicating which battery is a target battery in each switching period or groups formed by grouping the batteries,
A battery state estimation device.
15. The method of claim 14,
The processor groups the subsets of the batteries into groups and assigns each of the battery models to each of the groups.
A battery state estimation device.
18. The method of claim 17,
Wherein the processor determines state information of a corresponding target battery of each group in each switching period based on sensing data of a battery model assigned to each group and a corresponding target battery of each group,
A battery state estimation device.
18. The method of claim 17,
The number of groups being equal to or less than the number of batteries,
A battery state estimation device.
18. The method of claim 17,
The processor regroups subsets of the batteries when state information of each of the batteries in the last switching period is determined and a group update event occurs,
Wherein the group update event is generated based on at least one of a predetermined period, a travel distance of the vehicle using the batteries as a power source, and the determined state information.
A battery state estimation device.
18. The method of claim 17,
Wherein the processor groups the subsets of the batteries based on at least one of the voltage magnitude and the previous state information of each of the batteries.
A battery state estimation device.
22. The method of claim 21,
Wherein the previous state information includes at least one of charge state information, life state information, and abnormality state information of each of the batteries determined before the first switching period of the switching periods.
A battery state estimation device.
18. The method of claim 17,
Wherein the processor determines state information of the non-target battery by adding the state change amount to previous state information of the non-target battery,
A battery state estimation device.
15. The method of claim 14,
The battery model is an electrochemical model,
A battery state estimation device.

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