Patent Application
20190190091
The following description relates to battery state estimation.
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.
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.
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.
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.
Referring to
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
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
Referring to
In the example of
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 T1 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 T1 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 T1 based on a state variation Δ1. Δ1 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 T1 by adding the state variation Δ1 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 T1.
In the above Table 1, α1 denotes a SOC of the battery 1 in the switching period T1, the SOC determined by the battery model, and SOCN-1 #2, SOCN-1 #3, . . . , and SOCN-1 #96 denote SOCs of the batteries 2 through 96 in the (N−1)-th order update period, respectively.
In an example, Δ1 denotes a current-integrated quantity during the switching period T1. The battery state estimating apparatus 120 calculates Δ1 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 T1. For example, the battery state estimating apparatus 120 calculates Δ1 using the following Equation 1.
In Equation 1, t1 denotes a start time of a 1st switching period, t2 denotes a finish time of the 1st 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
The battery state estimating apparatus 120 determines state information of the target battery, that is, the battery 2, in the switching period T2 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 T2 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 T2 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
Referring back to
The following Table 2 shows an example of the state information of the batteries 1 through 96 in the switching period T2.
In the above Table 2, α2 denotes a SOC of the battery 2 in the switching period T2, the SOC determined by the battery model.
With respect to switching periods T3 through T96, 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 T96.
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.
Referring to
In operation 420, the battery state estimating apparatus 120 determines state information of the batteries 1 through 96 in each of switching periods T97 through T192 based on the updated order.
The battery state estimating apparatus 120 repeats the operations described with respect to
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
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
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
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
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
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
The number of members of the group 1 and/or the group 4 described with reference to
If the number of battery models is “3” in the example of
In another example, the battery state estimating apparatus 120 divides the battery cells into N or fewer groups. Unlike the description with reference to
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
In examples of
A hatched region of
The following Table 5 shows an example of state information of the battery cells 1 through 40 in a last switching period T37.
In the above Table 5, αT 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. βT 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, γT 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 αT, βT, γT, and δT denotes a switching period.
The description provided with reference to
In examples of
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
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
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
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.
Referring to
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
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
The battery state estimating method is performed by the battery state estimating apparatus 120.
Referring to
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
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
Referring to
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
The method illustrated in
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.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2018-0037674 | Mar 2018 | KR | national |
| 10-2018-0117812 | Oct 2018 | KR | national |
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.
| Number | Date | Country | |
|---|---|---|---|
| 62599856 | Dec 2017 | US |
