Battery state estimation method and system based on composite random variable

Abstract

To provide a battery module charging state estimation method.SOLUTION: The present disclosure method comprises: causing a microcontroller unit to acquire a composite random variable model associated with a plurality of battery cells included in a specific battery module; causing the microcontroller unit to receive voltage measurement data of a first battery cell and voltage measurement data a second battery cell among the plurality of battery cells included in the specific battery module; and causing at least one of the microcontroller unit and a neural processing unit to estimate the SOC of the specific battery module using a Kalman filter calculation based on the composite random variable model, the voltage measurement data of the first battery cell, and the voltage measurement data of the second battery cell.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a method for accurately estimating a battery state, and more particularly to a method for accurately estimating a battery state by Kalman filter calculation based on complex random variables. [Background technology]

[0002] Unlike primary batteries, which cannot be recharged, secondary batteries are batteries that can be charged and discharged. Low-capacity secondary batteries are used in small portable electronic devices such as smartphones, feature phones, laptop computers, digital cameras, and camcorders, while large-capacity secondary batteries are widely used as motor drive power sources and power storage batteries in hybrid vehicles, electric vehicles, and the like. Such secondary batteries include an electrode assembly consisting of a positive electrode and a negative electrode, a case that houses the electrode assembly, and electrode terminals connected to the electrode assembly.

[0003] A battery system contains multiple battery cells, and it is important to accurately grasp and manage the status of each cell. In particular, electric vehicles use battery packs containing hundreds of cells, so it is essential to accurately estimate the status of each cell. Monitoring all of the multiple battery cells requires a large number of sensors and computing resources, and the use of these resources affects the cost and performance of the product, so optimization of hardware and computing resources is necessary.

[0004] The foregoing information disclosed in this Background of the Invention section is intended solely to enhance understanding of the background of the present invention and may therefore include information that does not constitute prior art. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Korean Patent Publication No. 10-2015-0133065 Summary of the Invention [Problem to be solved by the invention]

[0006] To solve the above problems, the present disclosure provides a method for estimating the state of charge of a battery module, a computer program stored on a recording medium, and an apparatus (system).

[0007] However, the technical problems that the present invention aims to solve are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description of the invention provided below. [Means for solving the problem]

[0008] In order to solve the above technical problems, a method for estimating a state of charge of a battery module according to one embodiment of the present invention includes the steps of: a microcontroller unit (MCU) acquiring a composite random variable model associated with a plurality of battery cells included in a specific battery module; a microcontroller unit receiving voltage measurement data of a first battery cell among the plurality of battery cells included in the specific battery module; a microcontroller unit receiving voltage measurement data of a second battery cell among the plurality of battery cells included in the specific battery module; at least one of the microcontroller unit or a neural processing unit (NPU) estimating a State of Charge (SOC) of the specific battery module by a Kalman filter calculation based on the composite random variable model, the voltage measurement data of the first battery cell, and the voltage measurement data of the second battery cell; and outputting the estimated SOC of the specific battery module.

[0009] A computer program stored on a computer-readable recording medium is provided for executing a method according to an embodiment of the present invention on a computer.

[0010] A battery system according to an embodiment of the present invention includes a specific battery module including a plurality of battery cells, and a battery management master module including a microcontroller unit and a neural processing unit, wherein the microcontroller unit is configured to obtain a composite random variable model associated with the plurality of battery cells included in the specific battery module, the microcontroller unit receives, from the specific battery module, voltage measurement data of a first battery cell among the plurality of battery cells included in the specific battery module, the microcontroller unit receives, from the specific battery module, voltage measurement data of a second battery cell among the plurality of battery cells included in the specific battery module, at least one of the microcontroller unit or the neural processing unit estimates a SOC of the specific battery module by Kalman filter calculation based on the composite random variable model, the voltage measurement data of the first battery cell, and the voltage measurement data of the second battery cell, and the microcontroller unit outputs the estimated SOC of the specific battery module. [Effects of the Invention]

[0011] According to various embodiments of the present disclosure, the battery management master module can quickly and accurately estimate the SOC of each battery module without measuring and monitoring all of the status information of the battery cells included in each battery module.

[0012] According to various embodiments of the present disclosure, an NPU (Neural Processing Unit) processes matrix calculations required in the SOC estimation process of a battery module, and through MCU-NPU co-processing in which an MCU (Micro Controller Unit) and an NPU provide feedback to each other, a battery management master module can quickly, accurately, and resource-efficiently process the SOC estimation process of each battery module based on voltage measurement data of some battery cells.

[0013] According to various embodiments of the present disclosure, when estimating the SOC of a battery module, the voltage of each battery cell can be more accurately estimated by taking into account not only the voltage characteristics of the specific type of battery cell itself but also the characteristics of a sensor measuring the battery cell, thereby enabling more accurate estimation of battery state information such as the SOC and SOH of the battery module.

[0014] According to various embodiments of the present disclosure, the MCU performs main computational tasks such as pre-processing of the Kalman filter computation of the NPU, and can optimize the Kalman filter computation through co-processing with the NPU.

[0015] According to various embodiments of the present disclosure, the SOC of a battery module can be accurately and quickly estimated without measuring the voltages of all of the battery cells included in a particular battery module or performing a Kalman filter calculation on all of the battery cells. In particular, when a battery module has multiple battery cells connected in series, the SOC of the battery module can be more accurately estimated using only two voltage measurement data points, one for the first battery cell and the other for the last battery cell. This reduces processing time and power consumption when estimating the SOC of the battery module, thereby improving the overall performance, lifespan, and durability of a device (e.g., an electric vehicle) equipped with a battery system.

[0016] According to various embodiments of the present disclosure, by utilizing an information processing system connected to a vehicle via a network, which overcomes the performance limitations of modules within the vehicle, a battery management master module can generate a battery module SOC estimate more quickly and accurately. In addition, because the information processing system is located outside the vehicle, it can compare and analyze information about the vehicle with information about other vehicles in real time. This allows the information processing system to manage battery status information of multiple vehicles in an integrated manner.

[0017] However, the effects obtained by the present invention are not limited to the effects described above, and other technical effects not mentioned should be clearly understood by those skilled in the art from the description of the invention described below. [Brief explanation of the drawings]

[0018] The following drawings and the like of this application illustrate preferred embodiments of the present invention and, together with the detailed description of the invention to be given later, serve to further understand the technical concept of the present invention. Therefore, the present invention should not be interpreted as being limited to only the matters described in these drawings. [Figure 1] 1 is a configuration diagram of a battery system according to an embodiment of the present invention. [Figure 2] 1 is a block diagram of a battery system according to an embodiment of the present invention. [Figure 3] 1 is a block diagram of a battery system that estimates the SOC of each battery module according to an embodiment of the present invention. [Figure 4] FIG. 1 is a diagram illustrating a composite random variable model used to estimate the SOC of a battery module according to an embodiment of the present disclosure. [Figure 5] FIG. 10 is a diagram illustrating an example of estimating the SOC of a battery module according to an embodiment of the present disclosure. [Figure 6] FIG. 10 is a diagram illustrating an example of a method for estimating the SOC of a battery module based on estimated voltage values ​​of some battery cells according to an embodiment of the present disclosure. [Figure 7]3 is a flowchart illustrating a method for estimating a state of charge of a battery module according to an embodiment of the present disclosure. [Figure 8] FIG. 1 illustrates an example of an NPU included in a vehicle according to some embodiments of the present disclosure. [Figure 9] FIG. 1 illustrates an example of communication between a vehicle and an information processing system according to some embodiments of the present disclosure. [Figure 10] 1 is a diagram showing a battery pack according to an embodiment of the present invention; [Figure 11] 1 is a diagram showing a battery pack according to an embodiment of the present invention; [Figure 12] 1 is a diagram showing a vehicle body and a vehicle body part equipped with a battery pack according to an embodiment of the present invention. [Figure 13] 1 is a diagram showing a vehicle body and a vehicle body part equipped with a battery pack according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] <Summary of the Invention> According to one embodiment of the present disclosure, the first battery cell is the first battery cell of a plurality of battery cells connected in series, and the second battery cell is the last battery cell of the plurality of battery cells connected in series.

[0020] According to one embodiment of the present disclosure, the microcontroller unit does not receive voltage measurement data associated with a portion of the remaining battery cells of the plurality of battery cells excluding the first battery cell and the second battery cell.

[0021] According to one embodiment of the present disclosure, a first battery cell is connected to a first voltage measurement sensor, a second battery cell is connected to a second voltage measurement sensor, and the remaining battery cells of the plurality of battery cells, excluding the first battery cell and the second battery cell, are not connected to any voltage measurement sensor.

[0022] According to one embodiment of the present disclosure, a composite random variable model is generated based on a cell voltage deviation model associated with a plurality of battery cells and a sensor error model associated with a voltage measurement sensor coupled to a portion of the plurality of battery cells.

[0023] According to one embodiment of the present disclosure, the composite random variable model is generated by assuming that the cell voltage deviation model and the sensor error model are stochastically independent of each other.

[0024] According to one embodiment of the present disclosure, the composite random variable model, the cell voltage deviation model, and the sensor error model are probability density functions that follow a Gaussian distribution.

[0025] According to one embodiment of the present disclosure, receiving voltage measurement data of the first battery cell includes receiving, by the microcontroller unit, a first voltage measurement value measured at a first time of the first battery cell, and receiving, by the microcontroller unit, a second voltage measurement value measured at a second time of the first battery cell.

[0026] According to one embodiment of the present disclosure, the step of estimating the SOC includes the steps of: a microcontroller unit generating parameters associated with a Kalman filter; a neural processing unit generating a voltage estimate (posteriori estimate) of the first battery cell by Kalman filter calculation based on the generated parameters and voltage measurement data of the first battery cell; and a neural processing unit generating a voltage estimate of the second battery cell by Kalman filter calculation based on the generated parameters and voltage measurement data of the second battery cell.

[0027] According to one embodiment of the present disclosure, the step of generating parameters includes a step of setting the mean value of the composite random variable model as an initial state estimate of the Kalman filter, or a step of generating the initial state estimate of the Kalman filter based on at least a portion of the voltage measurement data of the first battery cell or the voltage measurement data of the second battery cell.

[0028] According to one embodiment of the present disclosure, generating the parameters includes generating a measurement noise covariance of the Kalman filter based on a standard deviation value of the composite random variable model.

[0029] According to one embodiment of the present disclosure, the step of estimating the SOC further includes a step of estimating the SOC of the specific battery module by Kalman filter calculation based on the voltage estimate of the first battery cell, the voltage estimate of the second battery cell, the composite random variable model, and the generated parameters.

[0030] According to one embodiment of the present disclosure, the step of estimating the SOC further includes the steps of generating a voltage estimate for each of the remaining battery cells among the plurality of battery cells, excluding the first battery cell and the second battery cell, by Kalman filter calculation based on the voltage estimate for the first battery cell, the voltage estimate for the second battery cell, the composite random variable model, and the generated parameters, and estimating the SOC of the specific battery module based on the voltage estimate for each of the plurality of battery cells.

[0031] According to one embodiment of the present disclosure, the step of estimating the SOC further includes a step of estimating the SOC of the particular battery module based on an average value of the voltage estimate of the first battery cell and the voltage estimate of the second battery cell.

[0032] According to one embodiment of the present disclosure, the microcontroller unit performs cell balancing for a particular battery module based on the estimated SOC.

[0033] According to one embodiment of the present disclosure, the first battery cell is the first battery cell of a plurality of battery cells connected in series, and the second battery cell is the last battery cell of the plurality of battery cells connected in series.

[0034] According to one embodiment of the present disclosure, the microcontroller unit does not receive voltage measurement data associated with a portion of the remaining battery cells of the plurality of battery cells excluding the first battery cell and the second battery cell.

[0035] According to one embodiment of the present disclosure, a first battery cell is connected to a first voltage measurement sensor, a second battery cell is connected to a second voltage measurement sensor, and the remaining battery cells of the plurality of battery cells, excluding the first battery cell and the second battery cell, are not connected to any voltage measurement sensor.

[0036] <Detailed Description of the Invention> Preferred embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. First, the terms and phrases used in this specification and claims should not be construed as being limited to their ordinary and dictionary meanings, but should be construed in a way that is consistent with the technical concept of the present invention, based on the principle that an inventor can appropriately define the concepts of terms in order to best describe his or her invention. Therefore, it should be understood that the embodiments described in this specification and the configurations shown in the drawings are merely some preferred embodiments of the present invention, do not represent the entire technical concept of the present invention, and that various equivalents and modifications may exist as of the time of filing this application.

[0037] Also, as used in this specification, "comprise" and / or "comprising" specify the presence of stated shapes, numbers, steps, operations, components, elements and / or groups, but do not exclude the presence or addition of one or more other shapes, numbers, operations, components, elements and / or groups.

[0038] In order to facilitate understanding of the invention, the accompanying drawings may not be drawn to scale, and the dimensions of some components may be exaggerated. In addition, the same reference numerals are used to refer to the same components in different embodiments.

[0039] A statement that two comparison objects are "identical" means that they are "substantially identical." Therefore, being substantially identical can include cases where there is a deviation that is considered low in the art, for example, a deviation of 5% or less. Furthermore, a statement that a certain parameter is uniform in a given region can mean that the parameter is uniform on average.

[0040] Although terms such as "first" and "second" are used to describe various components, it is understood that these components are not limited by these terms. These terms are merely used to distinguish one component from another, and unless otherwise specified, a first component can be a second component.

[0041] Throughout the specification, unless specifically stated to the contrary, each element may be singular or plural.

[0042] The phrase "above (or below)" a component or "above (or below)" a component means that the component is not only placed in contact with the upper surface (or lower surface) of the component, but also means that other components may be interposed between the component and the component placed above (or below) the component.

[0043] Furthermore, when a component is described as being "coupled," "coupled," or "connected" to another component, it should be understood that the components may be directly coupled or connected to each other, but that other components may be "intervening" between the components, or that each component may be "coupled," "coupled," or "connected" via other components. Furthermore, when a part is said to be electrically coupled to another part, this includes not only direct coupling, but also coupling via an intermediate element.

[0044] Throughout the specification, "A and / or B" means A or B, or A and B, unless specifically stated to the contrary. That is, "and / or" includes all or any combination of the listed items. "C through D" means at least C and at most D, unless specifically stated to the contrary.

[0045] The terms used herein are for the purpose of describing embodiments of the present disclosure and are not intended to limit the present disclosure.

[0046] 1 is a configuration diagram of a battery system 100 according to an embodiment of the present invention. The battery system 100 can be configured to monitor the voltage of a battery cell to estimate the State of Charge (SOC) of the battery module, estimate the SOC of the battery module, and manage the charging and discharging of the battery.

[0047] 1, the battery system 100 may include one or more battery management modules 120_1, 120_2, ..., 120_N and a battery management master module 130. Here, each of the one or more battery management modules 120_1, 120_2, ..., 120_N may be connected to a battery module 110_1, 110_2, ..., 110_N including a plurality of battery cells and configured to monitor the charging state of the battery cells in the corresponding battery module. The plurality of battery cells in the battery modules 110_1, 110_2, ..., 110_N may be the same type of battery cells. Furthermore, the battery management master module 130 may be configured to receive status information of the battery cells associated with the corresponding battery management module from the one or more battery management modules 120_1, 120_2, ..., 120_N.

[0048] According to one embodiment, the battery system 100 may be configured such that the battery management master module 130 receives information related to the status of the battery cells from one or more battery management modules 120_1, 120_2, ..., 120_N in a daisy chain manner. As an example, the battery management modules may accumulate status information from previous battery management modules and transfer it to the next battery management module. For example, the first battery management module 120_1 may receive status information from the first battery module 110_1 and transfer it to the second battery management module 120_2. The second battery management module 120_2 may receive status information from the first battery module 110_1 and the second battery module 110_2 and transfer it to the next battery management module. The Nth battery management module 120_N may transfer status information from the first battery module 110_1 to the Nth battery module 110_N to the battery management master module 130. The specific method for transferring the state information of the battery cells in a daisy chain manner is not limited to the above example, and may be performed in various manners.

[0049] According to one embodiment, the battery system 100 may be configured such that the battery management master module 130 receives battery cell status information directly from each of the battery management modules 120_1, 120_2, ..., 120_N. For example, each of the battery management modules 120_1, 120_2, ..., 120_N may be configured to be directly communicatively coupled to the battery management master module 130, and each of the battery management modules 120_1, 120_2, ..., 120_N may transmit the battery cell status information and / or information related to a defect in the corresponding battery management module to the battery management master module 130. In one embodiment, each of the battery management modules 120_1, 120_2, ..., 120_N may be communicatively coupled to the battery management master module 130 via a communication line (e.g., CAN communication).

[0050] In one embodiment, the battery management master module 130 receives voltage measurement data of some battery cells from each battery management module 120_1, 120_2, ..., 120_N, and estimates the SOC of each battery management module 120_1, 120_2, ..., 120_N using the received voltage measurement data of some battery cells, a composite random variable model associated with the battery cells, and a Kalman filter operation. The battery management master module 130 can output the estimated SOC value of each battery management module 120_1, 120_2, ..., 120_N, or perform cell balancing based on the estimated SOC value.

[0051] According to one embodiment, each battery management module 120_1, 120_2, ..., 120_N can measure the real-time status of each connected battery module 110_1, 110_2, ..., 110_N. Here, the real-time status of each battery module 110_1, 110_2, ..., 110_N may include voltage information of at least some of the battery cells included in each module. Furthermore, if necessary for estimating the SOC of each battery module, the real-time status of each battery module 110_1, 110_2, ..., 110_N may include not only voltage but also current information and temperature information of at least some of the battery cells.

[0052] According to one embodiment, the battery management master module 130 receives status information of at least some of the battery cells included in the first battery module 110_1 from the battery management module 120_1 and can estimate the SOC of the first battery module 110_1 based on the status information. The battery management master module 130 can also estimate the SOC of the remaining battery management modules 120_2, ..., 120_N in a similar manner. Thereafter, the battery management master module 130 can perform cell balancing of the battery management modules 120_1, 120_2, ..., 120_N based on the SOC values ​​of the battery management modules. The battery management master module 130 can also output the estimated SOC values.

[0053] This configuration allows the battery management master module 130 to quickly and accurately estimate the SOC of each battery module without measuring and monitoring all of the state information of the battery cells included in each battery module.

[0054] 2 is a block diagram of a battery system 200 according to one embodiment of the present invention. As shown in FIG. 2, the battery management module 220 can be configured to measure the status of battery cells in the battery module 210 and transfer the measured status to the battery management master module 260 and / or a subsequent battery management module. For example, the battery management module 220 can receive battery cell status information from the battery module 210 and transfer the information to the battery management master module 260 and / or a subsequent battery management module. To this end, the battery management module 220 can include measurement interfaces 222_1, ..., 222_N, balancing circuits 224_1, ..., 224_N, analog front-ends (AFEs) 226, a microcontroller unit 228, an interface block 230, and a CAN communication module 232.

[0055] The battery management module 220 may be coupled to a battery module 210 including a plurality of battery cells and configured to monitor the status of the battery cells. For example, each measurement interface 222_1, ..., 222_N and each balancing circuit 224_1, ..., 224_N may measure the status of each battery cell of the battery module 210, such as the voltage, current, and temperature of the battery cell.

[0056] The analog front end 226 can measure the status of the battery cells, such as the voltage, current, and temperature, as analog signals, via each measurement interface 222_1, ..., 222_N, and convert the measured status signals into digital signals. The analog front end 226 can be an integrated circuit. For example, the analog front end 226 can receive status information, such as the voltage, current, and temperature of the battery cells, as analog signals from each measurement interface 222_1, ..., 222_N, and convert the received status information into digital signals. The analog front end 226 can transmit the converted digital signals to the microcontroller unit 228. While FIG. 2 illustrates one analog front end 226, the battery management module 220 is not limited thereto, and can also include multiple analog front ends. For example, the battery management module 220 can include as many analog front ends as there are battery cells.

[0057] The microcontroller unit 228 can monitor the status of the battery cells based on status information such as the voltage, current, and temperature of each battery cell received from the analog front end 226. For example, the microcontroller unit 228 can determine whether a battery cell is in an overvoltage state or an undervoltage state based on at least one of the status information such as the voltage, current, and temperature of each battery cell. As another example, the microcontroller unit 228 can sense a voltage difference between the battery cells based on at least one of the status information such as the voltage, current, and temperature of each battery cell. Furthermore, when the microcontroller unit 228 senses a voltage difference between the battery cells, it can adjust the voltage difference between the battery cells using the balancing circuits 224_1, ..., 224_N to balance the voltages between the battery cells.

[0058] According to one embodiment, the battery management module 220 may be configured to transfer information related to the status of the battery cells to the battery management master module 260 in a daisy-chain manner. Specifically, the microcontroller unit 228 may receive a signal output from a preceding battery management module (not shown) via the interface block 230 at the signal input 250. The microcontroller unit 228 may also transfer the battery cell status information to a subsequent battery management module (not shown) via the interface block 230 at the signal output 240. Here, the battery cell status information associated with the preceding battery management module may include, but is not limited to, accumulated battery cell status information for the preceding battery management modules connected in the daisy-chain manner. In this case, the first battery management module and the last battery management module may each be connected to the battery management master module 260.

[0059] According to one embodiment, the battery management module 220 can directly transfer status information of the battery module 210 (e.g., status information of the battery cells, etc.) and status information of the battery management module 220 (e.g., fault information of the battery management module, etc.) to the battery management master module 260. For example, the microcontroller unit 228 can transfer the status information of the battery module 210 and the status information of the battery management module 220 to the battery management master module 260 via the CAN communication module 232. At this time, all the battery management modules can directly communicate with the battery management master module 260 via CAN communication.

[0060] In one embodiment, the analog front end 226 measures the voltage status of some of the battery cells in the battery module 210, converts the measured voltage status into a digital signal, and transmits the digital signal to the battery management master module 260. That is, the battery management master module 260 may not receive voltage measurement data related to some of the battery cells. This may be due to a failure of some measurement interfaces, balancing circuits, analog front ends, etc. In this case, the battery management master module 260 may estimate the SOC of the battery module 210 based on the composite random variable model related to the battery cells and the received voltage measurement data of some of the battery cells.

[0061] In one embodiment, only a portion of the battery cells in the battery module 210 may be connected to a voltage measurement sensor. For example, only the first and last battery cells of the battery cells connected in series may be connected to the voltage measurement sensor. In this case, the battery management master module 260 may receive only the voltage measurement data of the first and last battery cells from the battery management module 220. That is, the battery management master module 260 may not receive the voltage measurement data of the remaining battery cells other than the first and last battery cells of the battery cells connected in series. In this case, the battery management master module 260 may estimate the SOC of the battery module 210 based on the composite random variable model associated with the battery cells, the voltage measurement data of the first and last battery cells. This configuration may reduce the number of voltage measurement sensors, measurement interfaces, and analog front ends used in the battery management module 220, thereby reducing manufacturing costs.

[0062] 3 is a block diagram of a battery system that estimates the SOC of each battery module according to an embodiment of the present invention. As shown in the figure, a battery management master module 320 can be connected to battery management modules 310_1, ..., 310_n. The battery management master module 320 can receive voltage measurement data of some battery cells from each battery management module 310_1, ..., 310_n, and generate and output an SOC estimate 330 for each battery module based on the received data.

[0063] In one embodiment, to generate the SOC estimate 330, the battery management master module 320 may include a microcontroller unit (MCU) 322 and a neural processing unit (NPU) 324. While FIG. 3 illustrates the battery management master module 320 including only the MCU 322 and the NPU 324, the battery management master module 320 is not limited to this. For example, the battery management master module 320 may further include additional components or may not include some of the MCU 322 and the NPU 324.

[0064] In one embodiment, the MCU 322 performs main logic operations in the process of estimating the SOC of each battery module based on voltage measurement data of some battery cells, and the NPU 324 assists the MCU 322 in performing vector and / or matrix operations required for voltage estimation of each battery cell, such as Kalman filter operations.

[0065] In one embodiment, the NPU 324 can perform a Kalman filter prediction step and an update step (correction step). That is, the NPU 324 can perform prediction and correction of the system's state variables and error covariance in the Kalman filter operation. The NPU 324 can also perform filtering to separate the actual target signal from input data containing noise. The NPU 324 can also efficiently perform correction of process noise and observation noise matrices generated when measuring the voltage of a battery cell.

[0066] With this configuration, the NPU 324 processes various tasks required in the SOC estimation process of the battery module, and through MCU-NPU co-processing in which the MCU 322 and the NPU 324 provide feedback to each other, the battery management master module 320 can quickly, accurately, and resource-efficiently process the process of estimating the SOC of each battery module based on voltage measurement data of some battery cells.

[0067] 4 is a diagram illustrating a composite random variable model 400 used to estimate the SOC of a battery module according to an embodiment of the present disclosure. As shown in the figure, the composite random variable model 400 may include a cell voltage deviation model 410 and a sensor error model 420. Here, the cell voltage deviation model 410 and the sensor error model 420 are probabilistically independent of each other. As an example, each of the composite random variable model 400, the cell voltage deviation model 410, and the sensor error model 420 may be a probability density function (PDF) including mean and standard deviation information for specific data.

[0068] In one embodiment, the composite random variable model 400 including the cell voltage deviation model 410 and the sensor error model 420 can be pre-generated before the SOC estimation process of the battery module. For example, the composite random variable model 400 can be pre-generated during the development / production step of the battery system.

[0069] In one embodiment, the cell voltage deviation model 410 may include voltage average and standard deviation information associated with a particular type of battery cell included in the battery module, i.e., the cell voltage deviation model 410 may be associated with the specification, manufacturing variations, etc. of a particular battery cell.

[0070] In one embodiment, the sensor error model 420 may include error mean value and standard deviation information of the measuring instrument (e.g., voltage measurement sensor) that measures the voltage of each battery cell. For example, if the cell voltage deviation model 410 is the observed object, the sensor error model 420 may correspond to the observer. That is, the SOC of the observed object, i.e., the battery module, can be estimated based on the composite random variable model 400 that takes into account not only information about the observed object but also information about the observer.

[0071] In one embodiment, the cell voltage deviation model 410 and the sensor error model 420 may follow a Gaussian distribution. For example, the cell voltage deviation model

[0072]

number

[0073] is the average voltage

[0074]

number

[0075] , standard deviation

[0076]

number

[0077] The eigenvalues ​​can follow the following Gaussian distribution with

[0078]

number

[0079] Also, the sensor error model

[0080]

number

[0081] is the average sensor error

[0082]

number

[0083] , standard deviation

[0084]

number

[0085] The eigenvalues ​​can follow the following Gaussian distribution with

[0086]

number

[0087] At this time, the cell voltage deviation model

[0088]

number

[0089] and sensor error model

[0090]

number

[0091] If we assume that the variables are stochastically independent, then the composite random variable model

[0092]

number

[0093] is the average

[0094]

number

[0095] , standard deviation

[0096]

number

[0097] The eigenvalues ​​can follow the following Gaussian distribution with

[0098]

number

[0099] In one embodiment, based on the newly synthesized composite random variable model 400, the battery system (e.g., NPU 324 in FIG. 3) can perform a battery SOC estimation operation (e.g., a linear Kalman filter operation, a nonlinear Kalman filter operation (EKF, Extended Kalman Filter), etc.).

[0100] With this configuration, when estimating the SOC of the battery module, the voltage of each battery cell can be more accurately estimated by taking into account not only the voltage characteristics of the specific type of battery cell itself but also the characteristics of the sensor that measures the battery cell, thereby enabling more accurate estimation of battery state information such as the SOC and SOH of the battery module.

[0101] 5 is a diagram illustrating an example of estimating the SOC of a battery module according to an embodiment of the present disclosure. The MCU 322 and the NPU 324 are included in a battery management master module, and the battery management module 310_1 can communicate with the battery management master module via a communication module or the like.

[0102] In one embodiment, the MCU 322 can acquire 512 a composite random variable model, where the composite random variable model can be a probability density function generated based on a battery cell voltage deviation model and a sensor error model. For example, the composite random variable model can be a cell voltage average value.

[0103]

number

[0104] and the average sensor error

[0105]

number

[0106] The average value reflects both

[0107]

number

[0108] and the standard deviation of the cell voltage

[0109]

number

[0110] and sensor error standard deviation

[0111]

number

[0112] The standard deviation is reflected in

[0113]

number

[0114] and

[0115] In one embodiment, the battery management module 310_1 may measure (522) the voltages of some of the battery cells included in a specific battery module. Here, the MCU 322 may select (sample) some of the battery cells whose voltages the battery management module 310_1 will measure from among the battery cells included in the specific battery module. For example, each battery module may have multiple battery cells connected in series / parallel or a mixed series-parallel configuration. When multiple battery cells are connected in series to a single battery module, the MCU 322 may select the first and last battery cells from among the battery cells connected in series, and the battery management module 310_1 may measure the voltages of the two battery cells. The voltage measurement values ​​524 of the some battery cells may be transmitted to the MCU 322 and the NPU 324. The voltage measurement values ​​524 of the some battery cells may include voltage measurement data of a first battery cell associated with the first battery cell and voltage measurement data of a second battery cell associated with the last battery cell.

[0116] This configuration reduces the number of sensors, battery management module measurement interfaces, and analog front-ends (AFEs) used when monitoring battery cells. Even when sampling and measuring only the first and last battery cells instead of monitoring all battery cells connected in series within a battery module, the overall SOC state of the battery module can be accurately estimated. This reduces the hardware cost of the battery system, lowering the overall production cost, while optimizing the battery SOC estimation calculation performance and improving the reliability of the battery system.

[0117] 5 illustrates the battery management module 310_1 measuring the voltages of some cells after the MCU 322 acquires the composite random variable model and before the MCU 322 initializes the Kalman filter, and transferring the voltage measurement values ​​524 of some cells to the MCU 322 and the NPU 324. However, this is not limiting. For example, the battery management module 310_1 can also measure the voltages of some cells after the MCU 322 initializes the Kalman filter and before the MCU 322 performs Kalman filter prediction, and transfer the voltage measurement values ​​524 of some cells to the MCU 322 and the NPU 324.

[0118] In one embodiment, the MCU 322 may perform Kalman filter initialization 514 based on the acquired composite random variables and / or voltage measurements 524 of some cells to generate parameter values ​​required for Kalman filter operation, including a priori estimates, error covariances, system module factors, etc.

[0119] In one embodiment, the MCU 322 calculates an initial estimate of the state variables of the Kalman filter (here, the voltage values ​​of the battery cells).

[0120]

number

[0121] (That is, from the discrete time of the voltage of the battery cell sampled to measure the voltage among the multiple battery cells included in a specific battery module

[0122]

number

[0123] , from continuous time

[0124]

number

[0125] For example, the MCU322 can set the mean value in a compound random variable model.

[0126]

number

[0127] the initial estimate of the battery cell voltage

[0128]

number

[0129] As another example, the MCU 322 may set an initial estimate of the voltages of the battery cells based on the voltage measurements 524 of some of the cells received from the battery management module 310_1.

[0130]

number

[0131] The MCU 322 can also generate an initial estimate of the error covariance

[0132]

number

[0133] can be set arbitrarily. Here, the initial value of the error covariance can be set high and updated as the Kalman filter calculation progresses. Also, the MCU 322 can be used to calculate the composite random variable model

[0134]

number

[0135] Standard deviation of

[0136]

number

[0137] Based on the measurement noise covariance of the Kalman filter,

[0138]

number

[0139] Additionally, the MCU 322 can generate / set the parameter values ​​of the system model based on experimentally obtained preset values.

[0140] In one embodiment, the MCU 322 can store the obtained composite random variable model and the generated initial values ​​of the Kalman filter parameters in a memory or storage unit, and can transfer the composite random variable model 516 and the initialized Kalman filter parameters 518 to the NPU 324, which performs the Kalman filter calculation.

[0141] In one embodiment, the NPU 324 may perform 532 a Kalman filter prediction step based on the received composite random variable model 516 and the initialized Kalman filter parameters 518. For example, the NPU 324 may calculate an initial predicted value of the battery cell voltage using the following equation:

[0142]

number

[0143] is the mean value of the composite random variable model.

[0144]

number

[0145] The initial guess was set as

[0146]

number

[0147] Here, the predicted value may refer to an a priori estimate.

[0148]

number

[0149]

number

[0150] Here, A can represent the equation of motion of the system, i.e., a value that models the manner in which the voltage of the battery cell being measured changes over time.

[0151] In addition, the NPU 324 calculates the estimated value of the error covariance arbitrarily set by the MCU 322 using the following formula:

[0152]

number

[0153] Based on the initial estimate of the error covariance

[0154]

number

[0155] can be generated.

[0156]

number

[0157] where Q denotes the system noise covariance.

[0158] In one embodiment, the NPU 324 performs a complex random variable model

[0159]

number

[0160] A Kalman filter update step (correction step) may be performed (534) based on the Kalman filter parameters 518 and some battery cell voltage measurements 524. For example, the NPU 324 may update the battery cell voltage prediction values ​​and error covariance prediction values ​​generated in the Kalman filter prediction step to generate battery cell voltage estimates and error covariance estimates (a posteriori estimate).

[0161] As an example, the update step in the Kalman filter calculation is the Kalman gain

[0162]

number

[0163] Calculate the measurement value

[0164]

number

[0165] , predicted value

[0166]

number

[0167] and Kalman gain

[0168]

number

[0169] State variable estimates based on

[0170]

number

[0171] and update the error covariance

[0172]

number

[0173] The method may include updating the

[0174]

number

[0175]

number

[0176]

number

[0177] where:

[0178]

number

[0179] is the measurement noise covariance of the Kalman filter

[0180]

number

[0181] represents

[0182]

number

[0183] may represent a predefined matrix that represents the relationship between the measured voltage of the battery and the estimated voltage of the battery.

[0184] The error covariance generated along with the state variable estimates during the Kalman filter update step

[0185]

number

[0186] is the estimated value of the state variable

[0187]

number

[0188] As an example, the NPU 324 uses the corrected error covariance

[0189]

number

[0190] Based on the predetermined recursion termination threshold, it can be determined (536) whether the termination condition is satisfied. If it is determined that the termination condition is not satisfied, the NPU 324 can repeat the Kalman filter prediction execution (532) and the Kalman filter update execution (534) based on the voltage measurement values ​​of a new part of the cells. On the other hand, if it is determined that the termination condition is satisfied, the NPU 324 can terminate the Kalman filter operation. For example, the error covariance corrected n times

[0191]

number

[0192] is the default threshold

[0193]

number

[0194] If the voltage is lower than , the NPU 324 will interrupt the Kalman filter recursion and use the voltage estimates of some battery cells corrected up to now.

[0195]

number

[0196] It can output 538.

[0197] In one embodiment, when a termination condition terminates the Kalman filter operation, the NPU 324 can transfer voltage estimates 538 of the partial battery cells to the MCU 322. The voltage estimates 538 of the partial battery cells can include a voltage estimate of a first battery cell associated with the initial battery cell and a voltage estimate of a second battery cell associated with the last battery cell. The MCU 322 and / or the NPU 324 can then estimate 540 the SOC of the battery module.

[0198] With this configuration, the MCU 322 performs main calculation tasks such as preprocessing of the Kalman filter calculation of the NPU 324, and the Kalman filter calculation can be optimized through co-processing of the NPU 324, thereby improving calculation efficiency.

[0199] 6 illustrates an example method 600 for estimating the SOC of a battery module based on voltage estimates of some battery cells, according to one embodiment of the present disclosure. In one embodiment, method 600 may be performed by an MCU and / or an NPU. As shown, method 600 may begin with an NPU generating (610) voltage estimates of a first battery cell and a second battery cell. For example, as described above in FIG. 5, the NPU may generate voltage estimates of the first battery cell and the second battery cell when the Kalman filter operation is terminated due to a termination condition.

[0200] In one embodiment, the NPU can estimate 620 the voltages of the remaining battery cells included in the battery module using the voltage estimate of the first battery cell, the voltage estimate of the second battery cell, a composite probability model, and a Kalman filter operation, and can then estimate 630 the SOC of the battery module based on the voltage estimates of all battery cells included in the MCU and / or the battery module.

[0201] 6 illustrates the MCU and / or NPU estimating the SOC of the battery module based on the estimated voltage values ​​of all battery cells included in the battery module, but this is not limiting. For example, the MCU 322 and / or NPU can estimate (630) the SOC of the battery module based on the estimated voltage values ​​of the first battery cell and the second battery cell. As one example, the MCU 322 and / or NPU can estimate the SOC of the battery module using the estimated voltage values ​​of the first battery cell and the second battery cell, a composite random variable model, and a Kalman filter operation. As another example, the MCU 322 and / or NPU can estimate the SOC of the battery module based on the average value of the estimated voltage values ​​of the first battery cell and the second battery cell.

[0202] This configuration allows for accurate and rapid estimation of the SOC of a battery module without having to measure all of the voltages of the battery cells included in a particular battery module or perform Kalman filter calculations on all of the battery cells. In particular, when a battery module has multiple battery cells connected in series, the SOC of the battery module can be more accurately estimated using only two voltage measurement data points, one for the first battery cell and the other for the last battery cell. This reduces processing time and power consumption when estimating the SOC of the battery module, thereby improving the overall performance, lifespan, and durability of a device (e.g., an electric vehicle) equipped with a battery system.

[0203] FIG. 7 is a flowchart illustrating a method 700 for estimating the state of charge of a battery module according to one embodiment of the present disclosure. The method may be performed by at least one processor (e.g., a microcontroller unit or a neural processing unit) of a battery system or an information processing system external to a vehicle. Method 700 may begin by the microcontroller unit acquiring (710) a composite random variable model associated with a plurality of battery cells included in a particular battery module. Here, the composite random variable model may be a model generated based on a cell voltage deviation model associated with the plurality of battery cells and a sensor error model associated with a voltage measurement sensor coupled to some of the plurality of battery cells. For example, the composite random variable model may be generated by assuming that the cell voltage deviation model and the sensor error model are stochastically independent of each other. The composite random variable model, the cell voltage deviation model, and the sensor error model may be probability density functions according to Gaussian distributions.

[0204] In one embodiment, the microcontroller unit may receive (720) voltage measurement data for a first battery cell among a plurality of battery cells included in a particular battery module. Specifically, the microcontroller unit may receive multiple voltage measurements of the first battery cell measured at multiple times as the voltage measurement data. For example, the microcontroller unit may receive a first voltage measurement of the first battery cell measured at a first time and a second voltage measurement of the first battery cell measured at a second time.

[0205] Similarly, the microcontroller unit may receive (730) voltage measurement data of a second battery cell among the plurality of battery cells included in the particular battery module. Specifically, the microcontroller unit may receive multiple voltage measurements of the second battery cell measured at multiple times as the voltage measurement data. For example, the microcontroller unit may receive a first voltage measurement of the second battery cell measured at a first time and a second voltage measurement of the second battery cell measured at a second time.

[0206] In one embodiment, the first battery cell may be the first battery cell of a plurality of battery cells connected in series, and the second battery cell may be the last battery cell of a plurality of battery cells connected in series.

[0207] In one embodiment, the microcontroller unit may be configured to not receive voltage measurement data associated with a portion of the remaining battery cells of the plurality of battery cells, excluding the first battery cell and the second battery cell.

[0208] In one embodiment, the first battery cell is connected to a first voltage measurement sensor, the second battery cell is connected to a second voltage measurement sensor, and the remaining battery cells of the plurality of battery cells, excluding the first battery cell and the second battery cell, are not connected to any voltage measurement sensor.

[0209] In one embodiment, at least one of the microcontroller unit or the neural processing unit can estimate (740) the SOC of a particular battery module using a Kalman filter calculation based on the composite random variable model, the voltage measurement data of the first battery cell, and the voltage measurement data of the second battery cell.

[0210] In one embodiment, the step of estimating the SOC may include the steps of: a microcontroller unit generating parameters associated with a Kalman filter; a neural processing unit generating a voltage estimate (posteriori estimate) of the first battery cell using a Kalman filter operation based on the generated parameters and voltage measurement data of the first battery cell; and a neural processing unit generating a voltage estimate of the second battery cell using a Kalman filter operation based on the generated parameters and voltage measurement data of the second battery cell.

[0211] In one embodiment, generating the parameters may include setting the mean value of the composite random variable model as an initial state estimate of the Kalman filter, or generating the initial state estimate of the Kalman filter based on at least a portion of the voltage measurement data of the first battery cell or the voltage measurement data of the second battery cell.

[0212] In one embodiment, generating the parameters may include generating a measurement noise covariance of the Kalman filter based on a standard deviation value of the composite random variable model.

[0213] In one embodiment, the step of estimating the SOC may further include estimating the SOC of the specific battery module by Kalman filter calculation based on the voltage estimate of the first battery cell, the voltage estimate of the second battery cell, the composite random variable model, and the generated parameters.

[0214] In one embodiment, the step of estimating the SOC may further include the steps of generating a voltage estimate for each of the remaining battery cells among the plurality of battery cells, excluding the first battery cell and the second battery cell, by Kalman filter calculation based on the voltage estimate for the first battery cell, the voltage estimate for the second battery cell, the composite random variable model, and the generated parameters, and estimating the SOC of the specific battery module based on the voltage estimate for each of the plurality of battery cells.

[0215] In one embodiment, the step of estimating the SOC may further include the step of estimating the SOC of the particular battery module based on an average value of the voltage estimate of the first battery cell and the voltage estimate of the second battery cell.

[0216] In one embodiment, the microcontroller unit can perform cell balancing for a particular battery module based on the estimated SOC.

[0217] In one embodiment, the microcontroller unit can output the estimated SOC of a particular battery module.

[0218] 7 and the above description are merely illustrative and may be implemented differently in some embodiments, such as by omitting one or more steps, changing the order of steps, overlapping one or more steps, or repeating one or more steps.

[0219] 7 illustrates that the steps of method 700 are performed by a microcontroller unit and / or a neural processing unit within the battery management master module, but is not limited to this. For example, the steps of method 700 can be performed by an information processing system (e.g., an external server) external to the battery management master module and / or the vehicle.

[0220] 8 is a diagram illustrating an example in which an NPU is included in a vehicle 1000 according to some embodiments of the present disclosure. As shown in the figure, the vehicle 1000 may include a battery management master module 1200 and a battery 1400. The battery management master module 1200 may include a microcontroller unit (MCU) and a neural processing unit (NPU). The battery 1400 may include multiple battery modules. Each battery module may include multiple battery cells.

[0221] The battery management master module 1200 can receive voltage measurement data of some battery cells for each battery module. At this time, the MCU and NPU in the battery management master module 1200 can estimate the SOC of each battery module through co-processing of the voltage measurement data of some battery cells for each battery module. Based on the estimated SOC of the multiple battery modules, the battery management master module 1200 can perform cell balancing for the multiple battery modules.

[0222] FIG. 9 is a diagram illustrating an example of communication between a vehicle 1000 and an information processing system 2000 according to some embodiments of the present disclosure. As shown in the figure, the vehicle 1000 may include a battery management master module 1200, a battery 1400, and a communication unit (not shown). The vehicle 1000 may transmit and receive data to and from the information processing system 2000 via a network using the communication unit. The information processing system 2000 and the battery management master module 1200 may each include at least one processor. For example, the battery management master module 1200 may include a microcontroller unit (MCU), and the information processing system 2000 may include a neural processing unit (NPU). The information processing system 2000 may be a server external to the vehicle.

[0223] The vehicle 1000 can transmit voltage measurement data of some battery cells for each battery module via a network to the information processing system 2000. The information processing system 2000 can include an operating system and at least one program code.

[0224] In one embodiment, the information processing system 2000 can perform some or all of the functions of the battery management master module 1200 shown in Fig. 8. For example, the information processing system 2000 can process voltage measurement data of some battery cells in each battery module to estimate the SOC of each battery module. The information processing system 2000 can transmit the estimated SOC of each battery module to the vehicle 1000. In this case, the battery management master module 1200 can perform cell balancing for the multiple battery modules based on the estimated SOC of the multiple battery modules.

[0225] With this configuration, the battery management master module 1200 can generate a battery module SOC estimate more quickly and accurately by utilizing the information processing system 2000 connected to the vehicle 1000 via a network, exceeding the performance limitations of the modules within the vehicle 1000. In addition, since the information processing system 2000 exists outside the vehicle 1000, it can compare and analyze information about the vehicle 1000 with information about other vehicles in real time. This allows the information processing system 2000 to manage battery status information of multiple vehicles in an integrated manner.

[0226] 10 and 11 are diagrams showing a battery pack according to an embodiment of the present invention.

[0227] The battery pack may include a number of battery modules 50 and a housing 10 for accommodating the number of battery modules 50. For example, the housing 10 may include first and second housings 11 and 12 coupled to face each other with the number of battery modules 50 interposed therebetween. The number of battery modules 50 are electrically connected to each other using bus bars 51, and the number of battery modules 50 are electrically connected to each other in a series / parallel or series-parallel mixed mode to obtain a required electrical output.

[0228] 12 and 13 are diagrams showing a vehicle body and a vehicle body part equipped with a battery pack according to one embodiment of the present invention.

[0229] 12, a battery pack 91 may include a battery pack cover 13 that is part of a vehicle underbody 92, and a pack frame 20 that is disposed under the vehicle underbody 92. The pack frame 20 and the battery pack cover 13 may be structures that are integrally formed with the bottom 82 of the vehicle. Here, the pack frame 20 may refer to a housing for accommodating the battery modules in the battery pack.

[0230] A vehicle underbody 92 separates the interior and exterior of the vehicle, and the pack frame 20 may be located on the exterior of the vehicle.

[0231] FIG. 13 shows a schematic side view of a vehicle according to one embodiment of the present invention.

[0232] The vehicle 1000 is constructed by combining a body 99 with additional components such as a hood 97 at the front of the vehicle and fenders 98 located at the front and rear of the vehicle.

[0233] The vehicle 1000 may further include a vehicle bottom 82 and a battery pack cover 13, which are one of the vehicle body parts 90 including a battery pack 91 including the pack frame 20.

[0234] The above-described method may be provided as a computer program stored on a computer-readable recording medium for execution by a computer. The medium may continuously store a computer-executable program or temporarily store it for execution or download. The medium may also be various recording or storage means in the form of a single piece of hardware or multiple pieces of hardware combined together. The medium is not limited to media directly connected to a computer system but may also be distributed over a network. Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and ROMs, RAMs, flash memories, and other media configured to store program instructions. Other examples of media include recording or storage media managed by app stores that distribute applications and other sites or servers that provide or distribute various software.

[0235] The methods, operations, or techniques of the present disclosure can be implemented by a variety of means. For example, such techniques can be embodied in hardware, firmware, software, or a combination thereof. Those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in this disclosure can be embodied in electronic hardware, computer software, or a combination of both. To clearly illustrate this interchange between hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is embodied as hardware or software will vary depending on the particular application and design requirements imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementations should not be interpreted as departing from the scope of the present disclosure.

[0236] In a hardware implementation, the processing units utilized to perform the techniques may be embodied within one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or combinations thereof.

[0237] Accordingly, the various exemplary logic blocks, modules, and circuits described in this disclosure may be embodied or performed by any combination of general purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate and transistor logic, discrete hardware components, or any combination designed to perform the functions described herein. A general purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be embodied as a combination of computing devices, such as a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

[0238] In a firmware and / or software implementation, the techniques may be embodied as instructions stored on a computer-readable medium such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage device, etc. The instructions may be executable by one or more processors to cause the processors to perform certain aspects of the functions described in this disclosure.

[0239] If embodied as software, the techniques can be stored on or transmitted via a computer-readable medium as one or more instructions or code. Computer-readable media includes any medium that facilitates transfer of a computer program from one place to another, including both computer storage media and communication media. Storage media can be any available medium that can be accessed by a computer. By way of non-limiting example, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to transport or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection can be properly termed a computer-readable medium.

[0240] For example, if software is transferred from a website, server, or other remote source using coaxial cable, fiber optic cable, lead wire, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, lead wire, Digital Subscriber Line, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. As used herein, "disk" and "disc" include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable medium.

[0241] A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium may be coupled to the processor such that the processor reads information from, and writes information to, the storage medium. Alternatively, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Alternatively, the processor and the storage medium may reside as discrete components in a user terminal.

[0242] Although the embodiments described above are described as utilizing aspects of the presently disclosed subject matter on one or more stand-alone computer systems, the present disclosure is not limited thereto and may be implemented in any computing environment, such as a network or distributed computing environment. Furthermore, aspects of the subject matter in the present disclosure may be implemented on multiple processing chips or devices, and storage may be shared across multiple devices. Such devices may include PCs, network servers, and handheld devices.

[0243] In this specification, the present disclosure has been described using limited examples and drawings, but it is not limited thereto, and it goes without saying that various modifications and variations can be made by a person having ordinary knowledge in the technical field to which the present invention pertains within the technical spirit of the present invention and the equivalent scope of the claims. [Explanation of symbols]

[0244] 100 Battery System 110 Battery Module 120 Battery Management Module 130 Battery Management Master Module

Claims

1. The microcontroller unit acquires a composite random variable model associated with a plurality of battery cells included in a particular battery module; The microcontroller unit receives voltage measurement data of a first battery cell among a plurality of battery cells included in the specific battery module; receiving, by the microcontroller unit, voltage measurement data of a second battery cell among a plurality of battery cells included in the specific battery module; At least one of the microcontroller unit or the neural processing unit estimates the SOC of the specific battery module by a Kalman filter calculation based on the composite random variable model, the voltage measurement data of the first battery cell, and the voltage measurement data of the second battery cell; and outputting the estimated SOC of the specific battery module.

2. the first battery cell is a first battery cell among the plurality of battery cells connected in series; The method of claim 1 , wherein the second battery cell is a last battery cell among the plurality of battery cells connected in series.

3. 2. The method of claim 1, wherein the microcontroller unit does not receive voltage measurement data associated with a portion of the remaining battery cells excluding the first battery cell and the second battery cell among the plurality of battery cells.

4. the first battery cell is coupled to a first voltage measurement sensor; the second battery cell is coupled to a second voltage measurement sensor; 2. The method of claim 1, wherein the remaining battery cells, excluding the first battery cell and the second battery cell, among the plurality of battery cells are not connected to a voltage measurement sensor.

5. 2. The method of claim 1, wherein the composite random variable model is generated based on a cell voltage deviation model associated with the plurality of battery cells and a sensor error model associated with a voltage measurement sensor coupled to a portion of the plurality of battery cells.

6. The method for estimating a state of charge of a battery module according to claim 5 , wherein the composite random variable model is generated under the assumption that the cell voltage deviation model and the sensor error model are stochastically independent of each other.

7. The method for estimating a state of charge of a battery module according to claim 5 , wherein the composite random variable model, the cell voltage deviation model, and the sensor error model are probability density functions that follow a Gaussian distribution.

8. The step of receiving voltage measurement data of the first battery cell includes: receiving, by the microcontroller unit, a first voltage measurement of the first battery cell measured at a first time; 2. The battery module state of charge estimation method of claim 1, further comprising: receiving, by the microcontroller unit, a second voltage measurement value of the first battery cell measured at a second time.

9. The step of estimating the SOC includes: the microcontroller unit generating parameters associated with a Kalman filter; The neural processing unit generates a voltage estimate of the first battery cell by a Kalman filter calculation based on the generated parameters and voltage measurement data of the first battery cell; 2. The battery module charge state estimation method according to claim 1, further comprising: generating, by the neural processing unit, a voltage estimation value of the second battery cell by Kalman filter calculation based on the generated parameters and voltage measurement data of the second battery cell.

10. The step of generating the parameters includes: setting the mean value of the composite random variable model as an initial state estimate for a Kalman filter; or 10. The battery module state of charge estimation method of claim 9, comprising generating an initial state estimate for a Kalman filter based on at least a portion of the voltage measurement data of the first battery cell or the voltage measurement data of the second battery cell.

11. 10. The battery module state of charge estimation method according to claim 9, wherein the step of generating the parameters includes the step of generating a measurement noise covariance of a Kalman filter based on a standard deviation value of the composite random variable model.

12. 10. The battery module state of charge estimation method of claim 9, wherein the step of estimating the SOC further comprises the step of estimating the SOC of the specific battery module by a Kalman filter operation based on the voltage estimate of the first battery cell, the voltage estimate of the second battery cell, the composite random variable model, and the generated parameters.

13. The step of estimating the SOC includes: generating a voltage estimate for each of the remaining battery cells, excluding the first battery cell and the second battery cell, among the plurality of battery cells by a Kalman filter calculation based on the voltage estimate for the first battery cell, the voltage estimate for the second battery cell, the composite random variable model, and the generated parameters; 10. The method of claim 9, further comprising: estimating an SOC of the particular battery module based on the voltage estimates of each of the plurality of battery cells.

14. 10. The battery module state of charge estimation method of claim 9, wherein the estimating the SOC further comprises estimating the SOC of the specific battery module based on an average value of the voltage estimate value of the first battery cell and the voltage estimate value of the second battery cell.

15. 2. The method of claim 1, further comprising the step of: the microcontroller unit performing cell balancing for the particular battery module based on the estimated SOC.

16. A computer readable computer program for performing the method of any one of claims 1 to 15 on a computer.

17. a specific battery module including a plurality of battery cells; a battery management master module including a microcontroller unit and a neural processing unit; The microcontroller unit obtains a composite random variable model associated with a plurality of battery cells included in the particular battery module; The microcontroller unit receives, from the specific battery module, voltage measurement data of a first battery cell among a plurality of battery cells included in the specific battery module; The microcontroller unit receives, from the specific battery module, voltage measurement data of a second battery cell among the plurality of battery cells included in the specific battery module; At least one of the microcontroller unit or the neural processing unit estimates the SOC of the specific battery module by a Kalman filter calculation based on the composite random variable model, the voltage measurement data of the first battery cell, and the voltage measurement data of the second battery cell; The battery system, wherein the microcontroller unit is configured to output the estimated SOC of the particular battery module.

18. the first battery cell is a first battery cell among the plurality of battery cells connected in series; The battery system of claim 17 , wherein the second battery cell is a last battery cell among the plurality of battery cells connected in series.

19. 18. The battery system of claim 17, wherein the microcontroller unit does not receive voltage measurement data associated with a portion of the remaining battery cells of the plurality of battery cells excluding the first battery cell and the second battery cell.

20. the first battery cell is coupled to a first voltage measurement sensor; the second battery cell is coupled to a second voltage measurement sensor; The battery system of claim 17 , wherein the remaining battery cells of the plurality of battery cells, excluding the first battery cell and the second battery cell, are not connected to a voltage measurement sensor.

Images