Patent Publication Number: US-2019190091-A1

Title: Method and apparatus estimating a state of battery

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 62/599,856 filed on Dec. 18, 2017, in the U.S. Patent and Trademark Office, and claims the benefit under 35 U.S.C. § 119(a) of Korean Patent Application No. 10-2018-0037674 filed on Mar. 30, 2018, and Korean Patent Application No. 10-2018-0117812 filed on Oct. 2, 2018 in the Korean Intellectual Property Office, the entire disclosures, all of which, are incorporated herein by reference for all purposes. 
    
    
     BACKGROUND 
     1. Field 
     The following description relates to battery state estimation. 
     2. Description of Related Art 
     A state of a battery is estimated using various methods. The state of the battery may be estimated by integrating currents of the corresponding batteries or by using a battery model such as, for example, an electrical circuit model or an electrochemical model. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
     In one general aspect, a processor-implemented battery state estimation method includes determining state information of plural batteries in each of plural switching periods and outputting state information of the batteries in a last switching period of the switching periods. The determining includes determining, for each switching period, state information of a target battery based on a battery model and sensing data of the target battery, and state information of a non-target battery based on a state variation of the non-target battery. A target battery set in a first switching period of the plural switching periods is set to be a non-target battery in a second switching period of the plural switching periods, and a non-target battery in the first switching period is set to be a target battery in the second switching period of the plural switching periods. 
     The battery state estimation method may further include updating a relationship set for the batteries based on any one or any combination of voltage values of the plural batteries and the state information of the plural batteries in the last switching period. 
     The set relationship may indicate an order indicating which of the plural batteries is the target battery in each of the plural switching periods, or which groups are formed by grouping the plural batteries. 
     The battery state estimation method may further include grouping subsets of the plural batteries into groups and assigning respective battery models to the groups. 
     The determining of the state information of the target battery may include determining state information of a corresponding target battery in each of the groups in each of the plural switching periods based on the respective battery model assigned to each group and sensing data of the corresponding target battery in each group. 
     A respective total number of members of each of the groups may be equal or different, and a respective total number of the groups may be less than or equal to a total number of the batteries. 
     The battery state estimation method may further include regrouping the subsets of batteries in response to a group update event occurring as the state information of the batteries in the last switching period is determined. The group update event may occur based on any one or any combination of any two or more of a preset interval, a travel distance of a vehicle using the plural batteries as a power source, and the determined state information. 
     The grouping and assigning may include grouping the subsets of the batteries based on any one or any combination of previous state information and voltage values of the batteries. 
     The previous state information may include any one or any combination of charge state information, health state information, and abnormality state information of the batteries determined before a first switching period of the switching periods. 
     The determining of the state information of the non-target batteries may include determining the state information of the non-target batteries in respective switching periods by adding the respective state variations to previous state information of the respective non-target batteries in the respective switching periods. 
     The plural batteries may be respective battery cells, battery modules, or battery packs. 
     The determining of the state information of the target battery may include determining state information of a different target battery in respective multiple different switching periods of the plural switching periods, the different target battery being a respective non-target battery in other respective switching periods of the plural switching periods. 
     The battery model may be an electrochemical model. 
     In another general aspect, an apparatus with battery state estimation includes a processor, to determine state information of plural batteries in each of plural switching periods. The processor is configured to determine, for each switching period, state information of a target battery based on a battery model and sensing data of the target battery, and state information of a non-target battery based on a state variation of the non-target battery, and output state information of the batteries in a last switching period of the switching periods. A target battery set in a first switching period of the plural switching periods is set to be a non-target battery in a second switching period of the plural switching periods, and a non-target battery in the first switching period is set to be a target battery in the second switching period of the plural switching periods. 
     The processor may be further configured to update a relationship set for the batteries based on any one or any combination of voltage values of the batteries and the state information of the batteries in the last switching period. 
     The relationship set may indicate an order indicating which of the plural batteries is the target battery in each of the plural switching periods, or which groups are formed by grouping the plural batteries. 
     The processor may be further configured to group subsets of the plural batteries into groups and assign battery models to the respective groups. 
     The processor may be further configured to determine state information of a corresponding target battery in each of the groups in the each of the plural switching periods based on the respective battery model assigned to each group and sensing data of the corresponding target battery in each group. 
     A respective total number of members of each of the groups may be equal or different, and a respective total number of the groups may be less than or equal to a total number of the batteries. 
     The processor may be further configured to regroup the subsets of the batteries in response to a group update event occurring as the state information of the batteries in the last switching period is determined. The group update event may occur based on any one or any combination of any two or more of a preset interval, a travel distance of a vehicle using the plural batteries as a power source, and the determined state information. 
     The processor may be further configured to group the subsets of the batteries based on any one or any combination of previous state information and voltage values of the batteries. 
     The previous state information may include any one or any combination of charge state information, health state information, and abnormality state information of the batteries determined before a first switching period of the switching periods. 
     The processor may be further configured to determine the state information of the non-target batteries in the respective switching periods by adding the respective state variations to previous state information of the respective non-target batteries in the respective switching periods. 
     The battery model may be an electrochemical model. 
     Other features and aspects will be apparent from the following detailed description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example of a battery system. 
         FIGS. 2 and 3  illustrate examples of using a battery state estimating apparatus to determine state information of batteries in a switching period. 
         FIG. 4  illustrates an example of using a battery state estimating apparatus to update order of batteries. 
         FIGS. 5 through 8  illustrate examples of using a battery state estimating apparatus to group batteries. 
         FIGS. 9A through 10B  illustrate examples of using a battery state estimating apparatus to determine state information of batteries in a group. 
         FIGS. 11A through 11C  illustrate examples of a group update of a battery state estimating apparatus. 
         FIG. 12  illustrates an example of a battery state estimating apparatus. 
         FIG. 13  illustrates an example of a battery state estimating method. 
         FIG. 14  illustrates an example of a vehicle. 
     
    
    
     Throughout the drawings and the detailed description, unless otherwise described or provided, the same drawing reference numerals will be understood to refer to the same elements, features, and structures. The drawings may not be to scale, and the relative size, proportions, and depiction of elements in the drawings may be exaggerated for clarity, illustration, and convenience. 
     DETAILED DESCRIPTION 
     The following detailed description is provided to assist the reader in gaining a comprehensive understanding of the methods, apparatuses, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatuses, and/or systems described herein will be apparent after an understanding of the disclosure of this application. For example, the sequences of operations described herein are merely examples, and are not limited to those set forth herein, but may be changed as will be apparent after an understanding of the disclosure of this application, with the exception of operations necessarily occurring in a certain order. Also, descriptions of features that are known in the art may be omitted for increased clarity and conciseness. 
     The features described herein may be embodied in different forms, and are not to be construed as being limited to the examples described herein. Rather, the examples described herein have been provided merely to illustrate some of the many possible ways of implementing the methods, apparatuses, and/or systems described herein that will be apparent after an understanding of the disclosure of this application. 
     Although terms such as “first,” “second,” and “third” may be used herein to describe various members, components, regions, layers, or sections, these members, components, regions, layers, or sections are not to be limited by these terms. Rather, these terms are only used to distinguish one member, component, region, layer, or section from another member, component, region, layer, or section. Thus, a first member, component, region, layer, or section referred to in examples described herein may also be referred to as a second member, component, region, layer, or section without departing from the teachings of the examples. 
     The terminology used herein is for describing various examples only, and is not to be used to limit the disclosure. The articles “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms “comprises,” “includes,” and “has” specify the presence of stated features, numbers, operations, members, elements, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, operations, members, elements, and/or combinations thereof. 
     The features of the examples described herein may be combined in various ways as will be apparent after an understanding of the disclosure of this application. Further, although the examples described herein have a variety of configurations, other configurations are possible as will be apparent after an understanding of the disclosure of this application. 
     Unless otherwise defined, all terms including technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which examples belong in view of the present disclosure. It will be further understood that terms, such as those defined in commonly-used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
     When describing the examples with reference to the accompanying drawings, like reference numerals refer to like constituent elements and a repeated description related thereto will be omitted. When it is determined that a detailed description of a known function or configuration makes its understanding unnecessarily ambiguous, it will be omitted too. 
       FIG. 1  illustrates an example of a battery system. 
     Referring to  FIG. 1 , a battery system  100  includes batteries  110 - 1  through  110 - n  and a battery state estimating apparatus  120 . 
     Each of the batteries  110 - 1  through  110 - n  may be a battery cell, a battery module or a battery pack. 
     The battery state estimating apparatus  120  senses the batteries  110 - 1  through  110 - n  using at least one sensor. In other words, the battery state estimating apparatus  120  collects sensing data of the batteries  110 - 1  through  110 - n . The sensing data includes, for example, any one or any combination of any two or more of voltage data, current data, and temperature data. However, the sensing data is not limited to the examples mentioned above. 
     The battery state estimating apparatus  120  sets a relationship for the batteries  110 - 1  through  110 - n . The relationship indicates, for example, an order indicating which battery is a target battery in each of switching periods, or groups formed by grouping the batteries  110 - 1  through  110 - n . For example, the battery state estimating apparatus  120  sets an order of the batteries  110 - 1  through  110 - n  or groups the batteries  110 - 1  through  110 - n  into groups based on any one or any combination of previous state information and voltage values of the batteries  110 - 1  through  110 - n . The previous state information includes any one or any combination of any two or more of charge state information, for example, states of charge (SOCs), health state information, for example, states of health (SOHs), and abnormality state information of the batteries  110 - 1  through  110 - n  determined before a first switching period of the switching periods. The order and the grouping will be described further later. 
     In response to setting the relationship for the batteries  110 - 1  through  110 - n , the battery state estimating apparatus  120  determines state information of the batteries  110 - 1  through  110 - n  in each switching period. For example, the battery state estimating apparatus  120  determines state information of a target battery in an individual switching period based on a battery model and sensing data of the target battery, and determines state information of a non-target battery in the individual switching period based on state information in a previous switching period. Further, a detailed description thereof will be provided with reference to  FIGS. 2 and 3 . In response to the batteries  110 - 1  through  110 - n  being divided into groups, the battery state estimating apparatus  120  determines state information of a target battery belonging to each group in the individual switching period based on a battery model assigned to each group and sensing data of the target battery belonging to each group, and determines state information of a non-target battery belonging to each group based on state information in a previous switching period. Further, a detailed description thereof will be provided with reference to  FIGS. 9A through 10B . 
     The battery state estimating apparatus  120  updates the relationship set for the batteries  110 - 1  through  110 - n  based on any one or any combination of voltage values of the batteries  110 - 1  through  110 - n  and state information of the batteries  110 - 1  through  110 - n  in the last switching period. The update changes the order of the batteries  110 - 1  through  110 - n . This order update will further be described later with reference to  FIG. 4 . Further, any one or any combination of members of each group, a number of members of each group, and a number of groups is changed by the update. The group update will further be described later with reference to  FIGS. 11A through 11C . 
       FIGS. 2 and 3  illustrate examples of a battery state estimating apparatus determining state information of batteries in respective switching periods. 
     Referring to  FIG. 2 , in operation  210 , the battery state estimating apparatus  120  determines state information of the batteries  110 - 1  through  110 - n  in each of plural switching periods. In detail, the battery state estimating apparatus  120  determines state information of a target battery in each switching period based on a battery model and sensing data of the target battery in each switching period. An order, to be further described, determines a battery corresponding to the target battery of each switching period. The battery model is, for example, an electrical circuit model or an electrochemical model. However, the battery model is not limited to the examples mentioned above. The battery state estimating apparatus  120  determines state information of a non-target battery in each switching period based on a state variation in each switching period. 
     In the example of  FIG. 2 , during an N-th order update period, an order of batteries  1  through  96  is the battery  1 , the battery  2 , . . . , the battery  96 . The term “order” with respect to the batteries  1  through  96  means an order that each battery is a target battery in the N-th order update period. The battery  1  is at a first ordinal position in the N-th order update period, and thus is a first target battery among the batteries  1  through  96 . That is, the battery  1  corresponds to a target battery in a first switching period T 1 . The battery  96  is at a last ordinal position in the N-th order update period, and thus is the last target battery. That is, the battery  96  corresponds to a target battery in the last switching period T 96 . 
     The order of the batteries  1  through  96  is updated periodically, thus an order update period refers to an interval for updating the order of the batteries  1  through  96 . 
     The battery state estimating apparatus  120  determines state information of the target battery, that is, the battery  1 , in the first switching period T 1  based on a battery model and sensing data of the battery  1 . For example, the battery state estimating apparatus  120  extracts sensing data corresponding to the switching period T 1  from the sensing data of the battery  1 , and inputs the extracted sensing data into the battery model. The battery model derives the state information of the battery  1  based on the input sensing data. 
     The battery state estimating apparatus  120  determines state information of non-target batteries, that is, the batteries  2  through  96 , in the switching period T 1  based on a state variation Δ 1 . Δ 1  will be described further later. For example, the battery state estimating apparatus  120  determines state information of the batteries  2  through  96  in the switching period T 1  by adding the state variation Δ 1  to state information of the batteries  2  through  96  in an (N−1)-th order update period. 
     The following Table 1 shows an example of the state information of the batteries  1  through  96  in the switching period T 1 . 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Battery 
                 State information 
               
               
                   
                   
               
             
            
               
                   
                 Battery 1 
                 α 1   
               
               
                   
                 Battery 2 
                 SOC N−1  #2 + Δ 1   
               
               
                   
                 Battery 3 
                 SOC N−1  #3 + Δ 1   
               
               
                   
                 Battery 4 
                 SOC N−1  #4 + Δ 1   
               
               
                   
                 . . . 
                 . . . 
               
               
                   
                 Battery 95 
                 SOC N−1  #95 + Δ 1   
               
               
                   
                 Battery 96 
                 SOC N−1  #96 + Δ 1   
               
               
                   
                   
               
            
           
         
       
     
     In the above Table 1, α 1  denotes a SOC of the battery  1  in the switching period T 1 , the SOC determined by the battery model, and SOC N-1  #2, SOC N-1  #3, . . . , and SOC N-1  #96 denote SOCs of the batteries  2  through  96  in the (N−1)-th order update period, respectively. 
     In an example, Δ 1  denotes a current-integrated quantity during the switching period T 1 . The battery state estimating apparatus  120  calculates Δ 1  based on a reference capacity and an amount of currents flowing in a battery pack including the batteries  1  through  96  during the switching period T 1 . For example, the battery state estimating apparatus  120  calculates Δ 1  using the following Equation 1. 
     
       
         
           
             
               
                 
                   
                     Δ 
                     1 
                   
                   = 
                   
                     
                       
                         ∫ 
                         
                           t 
                           1 
                         
                         
                           t 
                           2 
                         
                       
                        
                       Idt 
                     
                     
                       reference 
                        
                       
                           
                       
                        
                       capacity 
                     
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                      
                     
                         
                     
                      
                     1 
                   
                   ] 
                 
               
             
           
         
       
     
     In Equation 1, t 1  denotes a start time of a 1 st  switching period, t 2  denotes a finish time of the 1 st  switching period, and reference capacity is a preset value and denotes a total capacity of a battery of the same type as the batteries  1  through  96 . “I” denotes currents flowing in a battery pack. 
     In the example of  FIG. 2 , the battery  2  is at a second ordinal position, that is, an ordinal position corresponding to a second target battery. Thus, in the switching period T 2 , the target battery is switched or changed from the battery  1  to the battery  2 . In other words, the battery state estimating apparatus  120  selects the battery  2  as the target battery in the switching period T 2 , in view of the order. In the switching period T 2 , the batteries  1 , and  3  through  96  correspond to non-target batteries. 
     The battery state estimating apparatus  120  determines state information of the target battery, that is, the battery  2 , in the switching period T 2  based on the battery model and sensing data of the battery  2 . For example, the battery state estimating apparatus  120  extracts sensing data corresponding to the switching period T 2  from the sensing data of the battery  2 , and inputs the extracted sensing data into the battery model. The battery model derives the state information of the battery  2  in the switching period T 2  from the input sensing data. In this example, the battery state estimating apparatus  120  or the battery model corrects the state information of the battery  2 . 
     Hereinafter, the correction of the state information of the battery  2  will be described with reference to  FIG. 3 . In the switching period T 2 , the target battery is switched from the battery  1  to the battery  2 , and the battery model receives an input of sensing data  320  of the battery  2 , rather than sensing data  310  of the battery  1 . That is, there exists a discontinuity between the input data  310  and the input data  320  of the battery model at a switching time. If the battery model derives state information of the battery  2  from the input data  320  and outputs the derived state information in a situation in which such a discontinuity exists, the output of the battery model is discontinuous between T 1  and T 2 , as shown in graph  330 . In the example, the battery state estimating apparatus  120  corrects an output of the battery model in each switching period using a separately established correction model or filter, for example, a Kalman filter. As a result, the corrected output is continuous as shown in graph  340 . In implementation, a correction function may be built into the battery model, and the output of the battery model may be continuous as shown in the graph  340 . 
     Referring back to  FIG. 2 , the battery state estimating apparatus  120  determines state information of the non-target batteries, that is, the batteries  1 , and  3  through  96 , in the switching period T 2  based on a state variation Δ 2 . For example, the battery state estimating apparatus  120  determines the state information of the batteries  1 , and  3  through  96  in the switching period T 2  by adding the state variation Δ 2  to the state information of the batteries  1 , and  3  through  96  in the switching period T 1 . The description of Δ 1  applies to Δ 2 , and thus, a further detailed description of Δ 2  will be omitted. 
     The following Table 2 shows an example of the state information of the batteries  1  through  96  in the switching period T 2 . 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Battery 
                 State information 
               
               
                   
                   
               
             
            
               
                   
                 Battery 1 
                 α 1  + Δ 2   
               
               
                   
                 Battery 2 
                 α 2   
               
               
                   
                 Battery 3 
                 SOC N−1  #3 + Δ 1  + Δ 2   
               
               
                   
                 Battery 4 
                 SOC N−1  #4 + Δ 1  + Δ 2   
               
               
                   
                 . . . 
                 . . . 
               
               
                   
                 Battery 95 
                 SOC N−1  #95 + Δ 1  + Δ 2   
               
               
                   
                 Battery 96 
                 SOC N−1  #96 + Δ 1  + Δ 2   
               
               
                   
                   
               
            
           
         
       
     
     In the above Table 2, α 2  denotes a SOC of the battery  2  in the switching period T 2 , the SOC determined by the battery model. 
     With respect to switching periods T 3  through T 96 , the battery state estimating apparatus  120  operates as in the above method. 
     The following Table 3 shows an example of state information of the batteries  1  through  96  in the last switching period T 96 . 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 Battery 
                 State information 
               
               
                   
                   
               
             
            
               
                   
                 Battery 1 
                 α 1  + Δ 2  + . . . + Δ 96   
               
               
                   
                 Battery 2 
                 α 2  + Δ 3  + . . . + Δ 96   
               
               
                   
                 Battery 3 
                 α 3  + Δ 4  + . . . + Δ 96   
               
               
                   
                 Battery 4 
                 α 4  + Δ 5  + . . . + Δ 96   
               
               
                   
                 . . . 
                 . . . 
               
               
                   
                 Battery 95 
                 α 95  + Δ 96   
               
               
                   
                 Battery 96 
                 α 96   
               
               
                   
                   
               
            
           
         
       
     
     The battery state estimating apparatus  120  operates as described above, thereby quickly and accurately determining state information of the batteries  1  through  96 . 
     For ease of description, the state information of the batteries  1  through  96  for the N-th order update is expressed as shown in the following Table 4. 
     
       
         
           
               
               
             
               
                   
                 TABLE 4 
               
               
                   
                   
               
             
            
               
                   
                 SOC N  #1 = α 1  + Δ 2  + . . . + Δ 96   
               
               
                   
                 SOC N  #2 = α 2  + Δ 3  + . . . + Δ 96   
               
               
                   
                 SOC N  #3 = α 3  + Δ 4  + . . . + Δ 96   
               
               
                   
                 SOC N  #4 = α 4  + Δ 5  + . . . + Δ 96   
               
               
                   
                 . . . 
               
               
                   
                 SOC N  #95 = α 95  + Δ 96   
               
               
                   
                 SOC N  #96 = α 96   
               
               
                   
                   
               
            
           
         
       
     
       FIG. 4  illustrates an example of updating an order of batteries by a battery state estimating apparatus. 
     Referring to  FIG. 4 , in operation  410 , the battery state estimating apparatus  120  updates an order of the batteries  110 - 1  through  110 - n  based on state information of the batteries  110 - 1  through  110 - n  in the last switching period of a provided order update period. For example, the battery state estimating apparatus  120  updates or determines the order of the batteries  1  through  96  in ascending order or a descending order of SOC N  #1 through SOC N  #96. That is, the battery state estimating apparatus  120  arranges SOC N  #1 through SOC N  #96 in ascending order or a descending order, and updates or determines the order of the batteries  1  through  96  based on an arrangement result. In another example, the battery state estimating apparatus  120  updates or determines the order of the batteries  1  through  96  in an ascending or descending order of voltage values of the batteries  1  through  96 . In the example of  FIG. 4 , in a case in which a result of arranging SOC N  #1 through SOC N  #96 in a descending order is SOC N  #41 (maximum), SOC N  #22, SOC N  #33, SOC N  #14, . . . , SOCN #25, and SOC N  #66 (minimum), the battery state estimating apparatus  120  updates or determines the order of the batteries  1  through  96  to be  41 ,  22 ,  33 ,  14 , . . . ,  26 , and  66  based on the arrangement result. 
     In operation  420 , the battery state estimating apparatus  120  determines state information of the batteries  1  through  96  in each of switching periods T 97  through T 192  based on the updated order. 
     The battery state estimating apparatus  120  repeats the operations described with respect to  FIGS. 2 and 3  after an (N+1)-th order update period. 
       FIGS. 5 through 8  illustrate examples of a battery state estimating apparatus grouping batteries. 
     The battery state estimating apparatus  120  may include a plurality of battery models, and group the batteries  110 - 1  through  110 - n  to be assigned to a corresponding battery model. For example, in a case in which a battery pack  500  includes battery modules  1  through  4  and each of the battery modules  1  through  4  includes a plurality of battery cells, as shown in  FIG. 5 , the battery state estimating apparatus  120  forms groups by performing module-based grouping or cell-based grouping, and assigns the battery models to the groups. The module-based grouping will be described with reference to  FIGS. 6A through 6D , and the cell-based grouping will be described with reference to  FIGS. 7A through 7C . 
     In an example, the battery state estimating apparatus  120  may include the same number of battery models as a number of battery modules. That is, the number of battery models in the battery state estimating apparatus  120  may be equal to the number of battery modules. In this example, the battery state estimating apparatus  120  configures battery cells belonging to each battery module as a single group. In the example of  FIG. 6A , the number of battery models is “4,” and the number of battery modules is “4”. The battery state estimating apparatus  120  sets or configures battery cells belonging to a battery module  1  or the battery module  1  as a group  1 , and assigns a battery model  1  to the group  1 . The battery state estimating apparatus  120  sets battery cells belonging to a battery module  2  or the battery module  2  as a group  2 , and assigns a battery model  2  to the group  2 . Further, the battery state estimating apparatus  120  sets battery cells belonging to a battery module  3  or the battery module  3  as a group  3 , and assigns a battery model  3  to the group  3 . The battery state estimating apparatus  120  sets battery cells belonging to a battery module  4  or the battery module  4  as a group  4 , and assigns a battery model  4  to the group  4 . 
     In another example, the battery state estimating apparatus  120  may include a different number of battery models from a number of battery modules. That is, the number of battery models in the battery state estimating apparatus  120  may be different from the number of battery modules. 
     As an example, the number of battery models may be less than the number of battery modules. In this example, the battery state estimating apparatus  120  forms or interprets battery cells belonging to different battery modules as a single group. In the example of  FIG. 6B , the number of battery modules is “4,” and the number of battery models is “3”. In this example, the battery state estimating apparatus  120  sets battery cells belonging to a battery module  1  and battery cells belonging to a battery module  2  or the battery modules  1  and  2  as a group  1 , and assigns a battery model  1  to the group  1 . The battery state estimating apparatus  120  sets battery cells belonging to a battery module  3  or the battery module  3  as a group  2 , and assigns a battery model  2  to the group  2 . The battery state estimating apparatus  120  sets battery cells belonging to a battery module  4  or the battery module  4  as a group  3 , and assigns a battery model  3  to the group  3 . In the example of  FIG. 6C , the number of battery modules is “4,” and the number of battery models is “2”. In this example, the battery state estimating apparatus  120  forms battery cells belonging to battery modules  1  and  2  as a group  1 , and forms battery cells belonging to battery modules  3  and  4  as a group  2 . The battery state estimating apparatus  120  assigns a battery model  1  to the group  1 , and assigns a battery model  2  to the group  2 . 
     As another example, the number of battery models in the battery state estimating apparatus  120  may be greater than the number of battery modules. In this example, the battery state estimating apparatus  120  selects at least one battery module from the battery modules and assigns thereto two of more battery models. For example, the battery state estimating apparatus  120  selects the at least one battery module based on SOCs, SOHs, or abnormality states of the battery modules, or selects the at least one battery module at random. Then the battery state estimating apparatus  120  assigns at least two battery models to the selected at least one battery module. In the example of  FIG. 6D , the number of battery modules is “4” and the number of battery models is “5”. In this example, the battery state estimating apparatus  120  selects a battery module  2  having a highest SOC. The battery state estimating apparatus  120  assigns battery models  2  and  3  to the selected battery module  2 . 
     In an example, the battery state estimating 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 are equal to or different from each other. In this example, the battery state estimating apparatus  120  divides the battery cells into N groups. Here, a number of members of each group is equal or different. 
     First, a case in which the number of members of each group is equal will be described. The battery state estimating apparatus  120  groups the battery cells into the N groups such that each of the N groups includes the same number of members. That is, the number of battery cells belonging to each group is equal to M/N. In an example of  FIG. 7A , the number of battery models is “4,” and the number of battery cells is “40”. In this example, the battery state estimating apparatus  120  divides the “40” battery cells into “4” groups such that each group includes equally “10” members. The battery state estimating apparatus  120  assigns battery models  1  through  4  to the “4” groups, respectively. 
     The battery state estimating apparatus  120  may group the battery cells such that each group includes a different number of members. For example, the battery state estimating apparatus  120  divides or allocates the battery cells into N groups such that same groups includes fewer number of battery cells. In an example of  FIG. 7B , the battery state estimating apparatus  120  forms “8” battery cells with determined relatively high SOCs as a group  1 , and forms “7” battery cells with determined relatively low SOCs as a group  4 . The battery state estimating apparatus  120  forms “15” battery cells with determined relatively high SOCs, among remaining “25” battery cells, as a group  2 , and forms remaining “10” battery cells as a group  3 . Herein, relatively high and low determination of SOCs can be performed based on predetermined high or low thresholds. Intermediate predetermined levels may also be determined. The battery state estimating apparatus  120  assigns battery models  1  through  4  to the “4” groups, respectively. However, the number of members of each group is provided only as an example and thus is not limited to the above example. Unlike the example of  FIG. 7B , the number of members of the group  1  may be equal to the number of members of the group  4 , the number of members of the group  2  may be equal to the number of members of the group  3 , and the number of members of each of the group  1  and the group  4  may be less than the number of members of each of the group  2  and the group  3 . 
     The number of members of the group  1  and/or the group  4  described with reference to  FIG. 7B  may be “1”. For example, the battery state estimating apparatus  120  may form a group  1  including only a battery cell with the highest SOC as a member, a group  4  including only a battery cell with a lowest SOC as a member, and a group  2  and a group  3  including remaining battery cells based on SOCs of the remaining battery cells. 
     If the number of battery models is “3” in the example of  FIG. 7B , the battery state estimating apparatus  120  may form a group  1  including “10” battery cells with relatively high SOCs, a group  3  including “10” battery cells with relatively low SOCs, and a group  2  including remaining “20” battery cells. 
     In another example, the battery state estimating apparatus  120  divides the battery cells into N or fewer groups. Unlike the description with reference to  FIGS. 7A and 7B , in an example of  FIG. 7C , the battery state estimating apparatus  120  including “4” battery models divides the “40” battery cells into “3” groups, rather than “4” groups. In the example of  FIG. 7C , the number of members of the group  1  is “10”, the number of members of the group  2  is “20”, and the number of members of the group  3  is “10”. However, the number of members of each group is provided only as an example and thus is not limited to the above example. In this example, the battery state estimating apparatus  120  may select a group from the groups  1  through  3  in view of any one or any combination of SOCs, SOHs, and abnormality states, and assign two or more battery models to the selected group. When the battery state estimating apparatus  120  selects the group  1  with the highest SOCs, the battery state estimating apparatus  120  may assign the battery models  1  and  4  to the group  1 . The battery state estimating apparatus  120  may assign the battery model  2  to the group  2  and the battery model  3  to the group  3 . 
     In a case of cell-based grouping, the battery cells are grouped without limiting to physical structures. That is, battery cells belonging to the same battery module belong to different groups, and battery cells belonging to different battery modules may belong to the same groups. The battery state estimating apparatus  120  sets battery cells having similar performances or characteristics as a group, without limiting to physical structures, and estimates states of the battery cells in the corresponding group. Therefore, the accuracy of estimating the states of the battery cells in the corresponding group may improve. 
     The battery pack  500  described with reference to  FIG. 5  has multiple battery modules in which a plurality of battery cells are modularized. As shown in an example of  FIG. 8 , a plurality of battery cells are formed or allocated as a single battery pack  800 . In this example, the battery state estimating apparatus  120  groups the battery cells in the battery pack  800  using the cell-based grouping described above. 
       FIGS. 9A through 10B  illustrate examples of a battery state estimating apparatus determining state information of batteries in a group. 
     In examples of  FIGS. 9A through 9C , battery cells  1  through  10  are formed or allocated as a group  1 , and battery cells  11  through  20  are formed or allocated as a group  2 . Further, battery cells  21  through  30  are formed or allocated as a group  3 , and battery cells  31  through  40  are formed or allocated as a group  4 . The groups  1  through  4  are formed or allocated by the battery state estimating apparatus  120  performing the module-based grouping or the cell-based grouping described above. The battery state estimating apparatus  120  assigns battery models  1  through  4  to the groups  1  through  4 , respectively. The battery state estimating apparatus  120  performs the operations described with reference to  FIGS. 2 through 4  with respect to the groups  1  through  4 . That is, the battery state estimating apparatus  120  performs the operations described with reference to  FIGS. 2 through 4  with respect to each group, thereby determining state information of battery cells belonging to each group. Results of performing the operations by the battery state estimating apparatus  120  with respect to each group are shown in  FIGS. 9A through 9C . 
     A hatched region of  FIGS. 9A through 9C  indicates a battery cell of which state information is determined using a battery model. In other words, the hatched region of  FIGS. 9A through 9C  represents a target battery in each switching period. For example, in a switching period T 5 , regions corresponding to a battery  5  of a group  1 , a battery  15  of a group  2 , a battery  25  of a group  3 , and a battery  35  of a group  4  are marked with hatches. The hatches in the switching period T 5  indicates that in the switching period T 5 , state information of each of the battery  5 , the battery  15 , the battery  25 , and the battery  35  are determined by a battery model assigned to each group. In other words, in the switching period T 5 , a target battery of the group  1  is the battery cell  5 , a target battery of the group  2  is the battery cell  15 , a target battery of the group  3  is the battery  25 , and a target battery of the group  4  is the battery cell  35 . 
     The following Table 5 shows an example of state information of the battery cells  1  through  40  in a last switching period T 37 . 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 5 
               
               
                   
                   
               
               
                   
                 Groups 
                 Battery No. 
                 State information 
               
               
                   
                   
               
             
            
               
                   
                 Group 1 
                 Battery 1 
                 α 37   
               
               
                   
                   
                 Battery 2 
                 α 36  + Δ 37   
               
               
                   
                   
                 . . . 
                 . . . 
               
               
                   
                   
                 Battery 10 
                 α 28  + Δ 29  + . . . + Δ 37   
               
               
                   
                 Group 2 
                 Battery 11 
                 β 37   
               
               
                   
                   
                 Battery 12 
                 β 36  + Δ 37   
               
               
                   
                   
                 . . . 
                 . . . 
               
               
                   
                   
                 Battery 20 
                 β 28  + Δ 29  + . . . + Δ 37   
               
               
                   
                 Group 3 
                 Battery 21 
                 γ 37   
               
               
                   
                   
                 Battery 22 
                 γ 36  + Δ 37   
               
               
                   
                   
                 . . . 
                 . . . 
               
               
                   
                   
                 Battery 30 
                 γ 28  + Δ 29  + . . . + Δ 37   
               
               
                   
                 Group 4 
                 Battery 31 
                 δ 37   
               
               
                   
                   
                 Battery 32 
                 δ 36  + Δ 37   
               
               
                   
                   
                 . . . 
                 . . . 
               
               
                   
                   
                 Battery 40 
                 δ 28  + Δ 29  + . . . + Δ 37   
               
               
                   
                   
               
            
           
         
       
     
     In the above Table 5, α T  denotes state information of a target battery of a group  1  in a switching period, and is determined by a battery model  1  assigned to the group  1  in the switching period. β T  denotes state information of a target battery of a group  2  in a switching period, and is determined by a battery model  2  assigned to the group  2  in the switching period. 
     Further, γ T  denotes state information of a target battery of a group  3  in a switching period, and is determined by a battery model  3  assigned to the group  3  in the switching period. Or denotes state information of a target battery of a group  4  in a switching period, and is determined by a battery model  4  assigned to the group  4 . T in α T , β T , γ T , and δ T  denotes a switching period. 
     The description provided with reference to  FIGS. 1 through 8  are applicable to the description provided with reference to  FIGS. 9A through 9C , and thus duplicate description will be omitted herein for conciseness. 
     In examples of  FIGS. 10A and 10B , battery cells  1  through  10  are formed or allocated as a group  1 , battery cells  11  through  30  are formed or allocated as a group  2 , and battery cells  31  through  40  are formed or allocated as a group  3 . The groups  1  through  3  are formed or allocated by the battery state estimating apparatus  120  performing the module-based grouping or the cell-based grouping described above. The battery state estimating apparatus  120  assigns battery models  1  through  3  to the groups  1  through  3 . 
     The number of members of each of the groups  1  and  3  are less than the number of members of the group  2 . Thus, the number of times state information of the members of each of the groups  1  and  3  is determined by utilizing a battery model is greater than that for the members of the group  2 . That is, a total number of times a member of each of the groups  1  and  3  becomes a target battery is greater than a total number of times a member of the group  2  becomes a target battery. For example, a number of times state information of a battery cell  12  of the group  2  is determined by utilizing the battery model  2  assigned to the group  2  is “1”. However, a total number of times state information of each of a battery cell  2  of the group  1  and a battery cell  32  of the group  3  is determined by utilizing a battery model assigned to each group is “4”. That is, in the examples of  FIGS. 10A and 10B , a total number of the hatched regions with respect to each of the battery cell  2  and the battery cell  32  is “4”, which is greater than that of the battery cell  12  of the group  2 . Thus, states of the battery cells  2  and  32  are estimated more accurately. 
     Further, if the batteries  1  through  40  are grouped such that a portion of the groups includes fewer members, the battery state estimating apparatus  120  prevents over-discharging or over-charging of batteries in the group including fewer members. For example, when the battery cells  1  through  40  are being charged, the battery state estimating apparatus  120  more accurately and frequently checks state information of the battery cells belonging to the group  1  with relatively high SOCs using the battery model  1  assigned to the group  1 , thereby controlling charging to prevent over-charging of the battery cells belonging to the group  1 . When the battery cells  1  through  40  are being discharged, the battery state estimating apparatus  120  more accurately and frequently checks state information of the battery cells belonging to the group  3  with relatively low SOCs using the battery model  3  assigned to the group  3 , thereby controlling discharging to prevent over-discharging of the battery cells belonging to the group  3 . 
     The description provided with reference to  FIGS. 1 through 8  apply to the description provided with reference to  FIGS. 10A and 10B , and thus the duplicate description will be omitted herein for conciseness. 
       FIGS. 11A through 110  illustrate examples of a group update of a battery state estimating apparatus. 
     The battery state estimating apparatus  120  performs a group update in response to a group update event occurring during a current group update period. The group update occurs based on, for example, any one or any combination of a determined elapse of a predetermined time, travel distance of a vehicle, and SOH or deterioration degree. Referring to an example of  FIG. 11A , a length of the group update period corresponds to a first order update period through an N-th order update period. The first through N-th order updates are performed during the first group update period. When the N-th order update period elapses, the group update event occurs. Referring to an example of  FIG. 11B , the group update event occurs based on a travel distance of a vehicle on which the battery state estimating apparatus  120  is mounted. For example, when the travel distance of the vehicle is determined greater than or equal to a predetermined distance, e.g. 700 km, the group update event occurs. In this example, although a time corresponding to a length of the group update period described with reference to  FIG. 11A  does not elapse, the group update event may occur. Referring to an example of  FIG. 11C , the group update event occurs based on a SOH or deterioration degree of the battery pack  500  or  800 . For example, when a SOH of the battery pack  500  or  800  is less than 0.91, the group update event occurs. In this example, although a time corresponding to the length of the group update period described with reference to  FIG. 11A  does not elapse, the group update event may occur. 
     By the group update, any one or any combination of members belonging to each of groups, a number of members of each group, and a number of groups are changed. For example, suppose that during a first group update period, battery cells  1  through  10  belong to a group  1 , battery cells  11  through  20  belong to a group  2 , battery cells  21  through  30  belong to a group  3 , and battery cells  31  through  40  belong to a group  4 . In response to an occurrence of the group update event, the battery state estimating apparatus  120  regroups the battery cells  1  through  40  by performing the module-based grouping or the cell-based grouping described above. By the group update, members of each group and/or a number of members may be changed. For example, “9” battery cells may belong to a group  1 , “11” battery cells may belong to a group  2 , “13” battery cells may belong to a group  3 , and “7” battery cells may belong to a group  4 . Further, by the group update, a number of groups may be changed. For example, the number of groups may decrease from “4” to “3” or fewer, or increase to “5” or more. Also, although such a group update is performed, the members of each group may remain unchanged. 
       FIG. 12  illustrates an example of a battery state estimating apparatus. 
     Referring to  FIG. 12 , the battery state estimating apparatus  120  includes a memory  1210  and a processor  1220 . 
     The memory  1210  is connected to the processor  1220 , and stores instructions executable by the processor  1220 . The memory  1210  includes a non-transitory computer-readable medium, for example, a high-speed random-access memory and/or a non-volatile computer-readable storage medium. 
     The processor  1220  executes instructions for performing the at least one operation described with reference to  FIGS. 1 through 110 . For example, the processor  1220  determines state information of the batteries  110 - 1  through  110 - n  in each of switching periods. Here, the processor  1220  determines state information of a target battery in each switching period based on a battery model and sensing data of the target battery in each switching period, and determines state information of a non-target battery in each switching period based on a state variation of the non-target battery in each switching period. The processor  1220  outputs state information of the batteries  110 - 1  through  110 - n  in a last switching period of the switching periods. 
     The processor  1220  updates a relationship set for the batteries  110 - 1  through  110 - n  based on any one or any combination of voltage values of the batteries  110 - 1  through  110 - n  and the state information of the batteries  110 - 1  through  110 - n  in the last switching period. The relationship indicates, for example, the order or the groups described above. 
     The description provided with reference to  FIGS. 1 through 110  are applicable to the description provided with reference to  FIG. 12 , and thus duplicate description will be omitted herein for conciseness. 
       FIG. 13  illustrates an example of a battery state estimating method. 
     The battery state estimating method is performed by the battery state estimating apparatus  120 . 
     Referring to  FIG. 13 , in operation  1310 , the battery state estimating apparatus  120  determines state information of the batteries  110 - 1  through  110 - n  in each of switching periods. In detail, the battery state estimating apparatus  120  determines state information of a target battery in each switching period based on a battery model and sensing data of the target battery in each switching period in operation  1311 , and determines state information of a non-target battery in each switching period based on a state variation of the non-target battery in each switching period in operation  1312 . 
     In operation  1320 , the battery state estimating apparatus  120  outputs state information of the batteries  110 - 1  through  110 - n  in a last switching period of the switching periods. 
     The description provided with reference to  FIGS. 1 through 110  are applicable to the description provided with reference to  FIG. 13 , and thus duplicate description will be omitted herein for conciseness. 
     The battery state estimating apparatus  120  may be mounted on various electronic devices using batteries as a power source, for example, a vehicle, a walking assistance device, a drone, and a mobile terminal, and performs the operations described with reference to  FIGS. 1 through 13 . Hereinafter, a case in which the battery state estimating apparatus  120  is mounted on a vehicle will be described with reference to  FIG. 14 . The description of  FIG. 14  is also applicable to other electronic devices. 
       FIG. 14  illustrates an example of a vehicle. 
     Referring to  FIG. 14 , a vehicle  1400  includes the battery pack  500  or  800 , and a battery management system (BMS)  1410 . The vehicle  1400  is, for example, an electric vehicle or a hybrid vehicle. 
     The BMS  1410  monitors whether an abnormality occurs in the battery pack  500  or  800 , and prevents over-charging or over-discharging of the battery pack  500  or  800 . Further, the BMS  1410  performs thermal control with respect to the battery pack  500  or  800  in response to a measured temperature of the battery pack  500  or  800  exceeding a first temperature, for example, 40° C., or being less than a second temperature, for example, −10° C. In addition, the BMS  1410  performs cell balancing such that charge states of battery cells included in the battery pack  500  or  800  are equalized. 
     In an example, the BMS  1410  includes the battery state estimating apparatus  120 , and determines state information of the battery cells in the battery pack  500  or  800  through the battery state estimating apparatus  120 . The BMS  1410  may determine a maximum value, a minimum value, or an average value of the state information of the battery cells to be state information of the battery pack  500  or  800 . 
     The BMS  1410  transmits the state 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 the VCU of the vehicle  1400  outputs the state information of the battery pack  500  or  800  on a display of the vehicle  1400 . 
     The battery state estimating apparatus  120 , the BMS  1410  and other apparatuses, units, modules, devices, and other components described herein with respect to  FIGS. 1, 12 and 14  are implemented by hardware components configured to perform the operations described in this application that are performed by the hardware components. Examples of hardware components that may be used to perform the operations described in this application where appropriate include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more of the hardware components that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers. A processor or computer may be implemented by one or more processing elements, such as an array of logic gates, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field-programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices that is configured to respond to and execute instructions in a defined manner to achieve a desired result. In one example, a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer. Hardware components implemented by a processor or computer may execute instructions or software, such as an operating system (OS) and one or more software applications that run on the OS, to perform the operations described in this application. The hardware components may also access, manipulate, process, create, and store data in response to execution of the instructions or software. For simplicity, the singular term “processor” or “computer” may be used in the description of the examples described in this application, but in other examples multiple processors or computers may be used, or a processor or computer may include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components may be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components may be implemented by one or more processors, or a processor and a controller, and one or more other hardware components may be implemented by one or more other processors, or another processor and another controller. One or more processors, or a processor and a controller, may implement a single hardware component, or two or more hardware components. A hardware component may have any one or more of different processing configurations, examples of which include a single processor, independent processors, parallel processors, single-instruction single-data (SISD) multiprocessing, single-instruction multiple-data (SIMD) multiprocessing, multiple-instruction single-data (MISD) multiprocessing, and multiple-instruction multiple-data (MIMD) multiprocessing. 
     The method illustrated in  FIG. 13  that perform the operations described in this application are performed by computing hardware, for example, by one or more processors or computers, implemented as described above executing instructions or software to perform the operations described in this application that are performed by the method. For example, a single operation or two or more operations may be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations may be performed by one or more processors, or a processor and a controller, and one or more other operations may be performed by one or more other processors, or another processor and another controller. One or more processors, or a processor and a controller, may perform a single operation, or two or more operations. 
     Instructions or software to control computing hardware, for example, one or more processors or computers, to implement the hardware components and perform the methods as described above may be written as computer programs, code segments, instructions or any combination thereof, for individually or collectively instructing or configuring the one or more processors or computers to operate as a machine or special-purpose computer to perform the operations that are performed by the hardware components and the methods as described above. In one example, the instructions or software include machine code that is directly executed by the one or more processors or computers, such as machine code produced by a compiler. In another example, the instructions or software includes higher-level code that is executed by the one or more processors or computer using an interpreter. The instructions or software may be written using any programming language based on the block diagrams and the flow charts illustrated in the drawings and the corresponding descriptions in the specification, which disclose algorithms for performing the operations that are performed by the hardware components and the methods as described above. 
     The instructions or software to control computing hardware, for example, one or more processors or computers, to implement the hardware components and perform the methods as described above, and any associated data, data files, and data structures, may be recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media. Examples of a non-transitory computer-readable storage medium include read-only memory (ROM), random-access memory (RAM), flash memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, and any other device that is configured to store the instructions or software and any associated data, data files, and data structures in a non-transitory manner and provide the instructions or software and any associated data, data files, and data structures to one or more processors or computers so that the one or more processors or computers can execute the instructions. In one example, the instructions or software and any associated data, data files, and data structures are distributed over network-coupled computer systems so that the instructions and software and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by the one or more processors or computers. 
     While this disclosure includes specific examples, it will be apparent after an understanding of the disclosure of this application that various changes in form and details may be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered in a descriptive sense only, and not for purposes of limitation. Descriptions of features or aspects in each example are to be considered as being applicable to similar features or aspects in other examples. Suitable results may be achieved if the described techniques are performed in a different order, and/or if components in a described system, architecture, device, or circuit are combined in a different manner, and/or replaced or supplemented by other components or their equivalents. Therefore, the scope of the disclosure is defined not by the detailed description, but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents are to be construed as being included in the disclosure.