Battery state estimation apparatus and method

Abstract

This disclosure has the advantage of more accurately estimating battery state by adding system noise to the recursive filter used for estimating battery state. According to embodiments of this disclosure, the battery state can be estimated more accurately because the parameters used in the extended Kalman filter are corrected to take into account the offset and variance of the current and voltage sensors.

Description

Technical Field

[0001] This application claims priority to Korean Patent Application No. 10-2022-0001089, filed in Korea on January 4, 2022, the disclosure of which is incorporated herein by reference.

[0002] This invention relates to a battery state estimation apparatus and method, and more specifically, to a battery state estimation apparatus and method capable of more accurately estimating battery state by adding system noise to a recursive filter used for estimating battery state. Background Technology

[0003] In recent years, the demand for portable electronic products such as laptops, cameras, and mobile phones has increased dramatically, and electric vehicles, energy storage batteries, robots, and satellites have also seen significant development. Therefore, high-performance batteries that allow for repeated charging and discharging are being actively researched.

[0004] Currently available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, and lithium batteries. Among them, lithium batteries have attracted much attention because, compared with nickel-based batteries, lithium batteries have almost no memory effect and also have extremely low self-discharge rates and high energy density.

[0005] Typically, battery state information such as State of Charge (SOC) and State of Health (SOH) can be estimated based on measurable voltage, current, and temperature. For example, an Extended Kalman filter (EKF), a type of recursive filter, can be used to estimate the battery's SOC and SOH. Here, SOC refers to the battery's current state of charge. SOH, on the other hand, is the battery's remaining lifespan and can refer to the battery's current lifespan due to degradation, assuming an initial lifespan of 100% at BOL (Beginning of Life).

[0006] In addition, in order to estimate battery state information more accurately, a dual adaptive extended Kalman filter that has been supplemented to the extended Kalman filter was studied (Non-Patent Literature 1).

[0007] Figure 1 This is a diagram illustrating the flowchart of a conventional dual adaptive extended Kalman filter. Specifically, Figure 1 It is the flowchart of non-patent document 1, and Figure 1 The “equation” described herein refers to the equation in Non-Patent Document 1.

[0008] Referring to Equations 12 and 13 of Non-Patent Literature 1, Q is used as process noise (system noise). However, Non-Patent Literature 1 only uses the multivariate normal distribution W. k ~N(0,Q) noise, and does not take into account the offset and variance of current and voltage sensors that may affect system noise.

[0009] In other words, in the process of estimating SOC and SOH, Non-Patent Literature 1 cannot reflect the offset of the current sensor used to measure the discharge current and / or charging current of the battery and the voltage sensor used to measure the voltage of the battery installed on the charging and discharging path of the battery. Therefore, there is a problem that the accuracy of the estimated SOC and SOH may be reduced.

[0010] (Non-patent literature 1) SOC estimation of lithium battery based on dual adaptive extended Kalman filter (IMMAEE2019, Yongliang Zheng et al., 2019). Summary of the Invention

[0011] Technical issues

[0012] This disclosure is designed to address problems in the relevant field, and therefore aims to provide a battery state estimation apparatus and method for more accurately estimating battery state by taking into account the offset and variance of current and voltage sensors.

[0013] These and other objects and advantages of this disclosure will become apparent from the following detailed description and will become more apparent from exemplary embodiments thereof. Moreover, it will be readily understood that the objects and advantages of this disclosure can be achieved by the means shown in the appended claims and combinations thereof.

[0014] Technical solution

[0015] A battery state estimation apparatus according to one aspect of this disclosure may include: an offset and variance calculation unit configured to calculate voltage offset and voltage variance based on battery voltage values ​​obtained during a predetermined time period, and to calculate current offset and current variance based on battery current values ​​obtained during the predetermined time period; a system noise calculation unit configured to calculate an offset noise matrix and a variance noise matrix based on voltage offset, voltage variance, a preset voltage measurement specification, current offset, current variance, and a preset current measurement specification, and to calculate system noise based on the offset noise matrix and the variance noise matrix; and a battery state estimation unit configured to estimate battery state information by applying system noise to a preset recursive filter.

[0016] The offset noise matrix can be configured as a matrix that includes a first offset component and a second offset component.

[0017] The system noise calculation unit can be configured to calculate a first offset component based on current offset and current measurement specifications, and to calculate a second offset component based on voltage offset and voltage measurement specifications.

[0018] The system noise calculation unit can be configured to calculate the first offset component using Equation 1 below.

[0019] [Equation 1]

[0020]

[0021] Where w1 is the first offset component, w 1_min It is the preset minimum value of the first offset component, w 1_max It is the preset maximum value of the first offset component, offset c It's current offset, range c It is the maximum measurable current according to current measurement specifications, and its accuracy is... c This refers to the current measurement error specified in the current measurement standard.

[0022] The system noise calculation unit can be configured to calculate the second offset component using Equation 2 below.

[0023] [Equation 2]

[0024]

[0025] Where w2 is the second offset component, w 2_min It is the preset minimum value of the second offset component, w 2_max It is the preset maximum value of the second offset component, offset. v It's voltage offset, range v It is the maximum measurable voltage according to voltage measurement specifications, and its accuracy is... v This refers to the voltage measurement error specified in the voltage measurement specifications.

[0026] The variance noise matrix can be configured as a matrix that includes a first variance component and a second variance component.

[0027] The system noise calculation unit can be configured to calculate a first variance component based on current variance and current measurement specifications, and to calculate a second variance component based on voltage variance and voltage measurement specifications.

[0028] The system noise calculation unit can be configured to calculate the first variance component using Equation 3 below.

[0029] [Equation 3]

[0030]

[0031] Where, q 11 It is the first variance component, q 11_min It is the preset minimum value of the first variance component, q 11_max It is the preset maximum value of the first variance component, varc It is the current variance, range c It is the maximum measurable current according to current measurement specifications, and its accuracy is... c This refers to the current measurement error specified in the current measurement standard.

[0032] The system noise calculation unit can be configured to calculate the second variance component using Equation 4 below.

[0033] [Equation 4]

[0034]

[0035] Where, q 22 It is the second variance component, q 22_min It is the preset minimum value of the second variance component, q 22_max It is the preset maximum value of the second variance component, var v It is the voltage variance, range v It is the maximum measurable voltage according to voltage measurement specifications, and its accuracy is... v This refers to the voltage measurement error specified in the voltage measurement specifications.

[0036] The system noise calculation unit can be configured to calculate system noise by calculating the dot product of the offset noise matrix, the variance noise matrix, and the transpose of the offset noise matrix.

[0037] The system noise calculation unit can be configured to calculate the system noise using the following equation 5.

[0038] [Equation 5]

[0039]

[0040] in, W is the system noise, and W is the offset noise matrix. T Q is the transpose of the offset noise matrix, and Q is the variance noise matrix.

[0041] The recursive filter can be configured as a dual adaptive extended Kalman filter, which includes a first extended Kalman filter that predicts and corrects the SOC and SOC covariance of the battery, and a second extended Kalman filter that predicts and corrects the SOH and SOH covariance of the battery.

[0042] The first extended Kalman filter can be configured to predict the SOC of the current cycle based on the SOC and offset noise matrix of the battery estimated in the previous cycle, predict the SOC covariance of the current cycle based on the SOC covariance of the battery estimated in the previous cycle and the system noise, and estimate the SOC and SOC covariance of the battery in the current cycle based on the predicted SOC, the predicted SOC covariance and the SOH predicted by the second extended Kalman filter.

[0043] The second extended Kalman filter can be configured to predict the SOH of the current cycle based on the SOH of the battery estimated in the previous cycle, predict the SOH covariance of the current cycle based on the SOH covariance and variance noise matrix of the battery estimated in the previous cycle, and estimate the SOH and SOH covariance of the battery in the current cycle based on the predicted SOH, the predicted SOH covariance, and the SOC predicted by the first extended Kalman filter.

[0044] A battery pack according to another aspect of this disclosure may include a battery state estimation device according to aspects of this disclosure.

[0045] An energy storage system according to another aspect of this disclosure may include a battery state estimation device according to aspects of this disclosure.

[0046] A battery state estimation method according to another aspect of this disclosure may include: a voltage and current acquisition step, acquiring the voltage and current values ​​of the battery; an offset and variance calculation step, calculating voltage offset and voltage variance based on the battery voltage values ​​acquired during a predetermined time period, and calculating current offset and current variance based on the battery current values ​​acquired during the predetermined time period; a noise matrix calculation step, calculating an offset noise matrix and a variance noise matrix based on the voltage offset, voltage variance, a preset voltage measurement specification, current offset, current variance, and a preset current measurement specification; a system noise calculation step, calculating system noise based on the offset noise matrix and the variance noise matrix; and a battery state information estimation step, estimating the battery state information by applying the system noise to a preset recursive filter.

[0047] Beneficial effects

[0048] According to one aspect of this disclosure, since the offsets and variances of the current and voltage sensors can be taken into account to correct the parameters used in the extended Kalman filter, there is an advantage that the battery state can be estimated more accurately.

[0049] The effects of this disclosure are not limited to those described above, and those skilled in the art will clearly understand from the appended claims other effects not mentioned herein. Attached Figure Description

[0050] The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, serve to provide a further understanding of the technical features of the present disclosure; therefore, the present disclosure should not be construed as limited to the drawings.

[0051] Figure 1 This is a diagram illustrating the flowchart of a conventional dual adaptive extended Kalman filter.

[0052] Figure 2 This is a diagram schematically illustrating a battery state estimation apparatus according to an embodiment of the present disclosure.

[0053] Figure 3 This is a diagram schematically illustrating an exemplary configuration of a battery pack according to another embodiment of the present disclosure.

[0054] Figure 4 This is a diagram schematically illustrating a battery state estimation method according to yet another embodiment of the present disclosure. Detailed Implementation

[0055] It should be understood that the terms used in this specification and the appended claims should not be construed as limited to their general or dictionary meanings, but rather as being interpreted based on their meanings and concepts corresponding to the technical aspects of this disclosure, on the basis of allowing the inventors to appropriately define the terms for the best interpretation.

[0056] Therefore, the description presented herein is merely a preferred example for illustrative purposes and is not intended to limit the scope of this disclosure. It should be understood that other equivalents and modifications may be made thereto without departing from the scope of this disclosure.

[0057] Furthermore, in describing this disclosure, detailed descriptions of known elements or functions are omitted where such descriptions would obscure the key subject matter of the disclosure.

[0058] Ordinal terms such as “first” and “second” can be used to distinguish one element from another among various elements, but are not intended to limit these elements.

[0059] Throughout this specification, when a part is referred to as “comprising” or “including” any element, it means that the part may further include other elements, without excluding other elements, unless otherwise specifically stated.

[0060] Furthermore, throughout this specification, when a part is referred to as "connected" to another part, it is not limited to the case where they are "directly connected," but also includes the case where they are "indirectly connected" and another element is inserted between them.

[0061] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0062] Figure 2 This is a schematic diagram illustrating a battery state estimation device 100 according to an embodiment of the present disclosure.

[0063] refer to Figure 2 The battery state estimation device 100 may include an offset and variance calculation unit 110, a system noise calculation unit 120, and a battery state estimation unit 130.

[0064] The offset and variance calculation unit 110 can be configured to calculate voltage offset and voltage variance based on the battery voltage values ​​obtained within a predetermined time period.

[0065] Here, a battery refers to a physically separable, independent single cell with a negative terminal and a positive terminal. For example, a lithium-ion battery or a lithium polymer battery can be considered a battery. Alternatively, a battery can refer to a battery module in which multiple cells are connected in series and / or parallel.

[0066] Preferably, the battery can be in an unloaded state. More preferably, the battery can be in a state where the open-circuit voltage (OCV) can be measured after a predetermined time in the unloaded state.

[0067] For example, the offset and variance calculation unit 110 can be communicatively connected to a voltage measurement unit that measures the voltage of a battery. Furthermore, the offset and variance calculation unit 110 can receive battery voltage values ​​measured within a predetermined time period from the voltage measurement unit. Here, the battery voltage values ​​received by the offset and variance calculation unit 110 can be multiple voltage values ​​measured by the voltage measurement unit within a predetermined time period according to a voltage measurement cycle.

[0068] For example, assuming the predetermined time period is T1, the voltage measurement period is T2, and T1 is greater than T2, the offset and variance calculation unit 110 can obtain multiple voltage values ​​measured during the T1 time period in the T2 period. Furthermore, the offset and variance calculation unit 110 can calculate multiple offsets of the voltage measurement unit during the T1 time period by calculating the difference between each of the multiple voltage values ​​measured during the T2 period and its corresponding predicted value. Additionally, the offset and variance calculation unit 110 can calculate the variance of the multiple calculated offsets as the voltage variance.

[0069] Typically, the offset of the voltage measurement unit can follow a normal distribution based on 0. That is, under normal circumstances, the offset is 0 [V], and when the measured value of the voltage measurement unit differs from the predicted value of the offset and variance calculation unit 110, the offset can be calculated as a value exceeding 0 [V].

[0070] For example, the offset and variance calculation unit 110 can calculate the offset of each of a plurality of voltage values ​​obtained during a predetermined time period, and determine the maximum value among the plurality of calculated offsets as the voltage offset. As another example, the offset and variance calculation unit 110 can determine the voltage offset as the average of the plurality of calculated offsets. Preferably, the offset and variance calculation unit 110 can determine the voltage offset as the average of the plurality of offsets so that a representative offset of the voltage measurement unit within the predetermined time period is determined as the voltage offset.

[0071] Furthermore, the offset and variance calculation unit 110 can calculate the voltage variance by calculating the variance of multiple calculated offsets.

[0072] For example, assuming the predetermined time period is 1 hour and the voltage measurement cycle is 1 minute, the offset and variance calculation unit 110 can obtain a total of 60 voltage values ​​within the predetermined time period (1 hour). The offset and variance calculation unit 110 can calculate a total of 60 offsets by calculating the offset of each of the 60 voltage values. Furthermore, the offset and variance calculation unit 110 can determine the voltage offset by the maximum or average value of the 60 offsets. Finally, the offset and variance calculation unit 110 can calculate the variance of the 60 offsets to determine the voltage variance.

[0073] The offset and variance calculation unit 110 can be configured to calculate current offset and current variance based on the current values ​​of the battery obtained within a predetermined time period.

[0074] For example, the offset and variance calculation unit 110 can be communicatively connected to a current measurement unit that measures the current of the battery. Furthermore, the offset and variance calculation unit 110 can receive the battery current values ​​measured by the current measurement unit within a predetermined time period. Here, the battery current values ​​received by the offset and variance calculation unit 110 can be multiple current values ​​measured by the current measurement unit within a predetermined time period according to a current measurement cycle.

[0075] For example, assuming the predetermined time period is T1, the current measurement period is T2, and T1 is greater than T2, the offset and variance calculation unit 110 can obtain multiple current values ​​measured during the T1 time period in the T2 period. Furthermore, the offset and variance calculation unit 110 can calculate multiple offsets of the current measurement unit during the T1 time period by calculating the difference between each of the multiple current values ​​measured in the T2 period and its corresponding predicted value. Additionally, the offset and variance calculation unit 110 can calculate the variance of the multiple calculated offsets as the current variance.

[0076] Typically, the offset of the current measurement unit can follow a zero-based normal distribution. That is, under normal conditions, the offset is 0 [mA], and when the measured value of the current measurement unit differs from the predicted value of the offset and variance calculation unit 110, the offset can be calculated as a value exceeding 0 [mA]. ​​For example, when the battery is in an unloaded state, the predicted current value can be 0 [mA]. ​​However, if leakage current occurs or a fault occurs in the current measurement unit, the measured current value may exceed 0 [mA]. ​​In this case, the offset of the current measurement unit calculated by the offset and variance calculation unit 110 may exceed 0 [mA].

[0077] For example, the offset and variance calculation unit 110 can calculate the offset of each of a plurality of current values ​​obtained during a predetermined time period, and determine the maximum value among the plurality of calculated offsets as the current offset. As another example, the offset and variance calculation unit 110 can determine the average of the plurality of calculated offsets as the current offset. Preferably, the offset and variance calculation unit 110 can determine the average of the plurality of offsets as the current offset so that a representative offset of the current measuring unit within the predetermined time period is determined as the current offset.

[0078] In addition, the offset and variance calculation unit 110 can calculate the current variance by calculating the variance of multiple calculated offsets.

[0079] For example, assuming the predetermined time period is 60 minutes and the current measurement cycle is 1 minute, the offset and variance calculation unit 110 can obtain a total of 60 current values ​​within the 60 minutes. The offset and variance calculation unit 110 can calculate a total of 60 offsets by calculating the offset of each of the 60 current values. Furthermore, the offset and variance calculation unit 110 can determine the current offset as the maximum or average value of the 60 offsets. Finally, the offset and variance calculation unit 110 can determine the current variance by calculating the variance of the 60 offsets.

[0080] The system noise calculation unit 120 can be configured to calculate the offset noise matrix and the variance noise matrix based on voltage offset, voltage variance, preset voltage measurement specifications, current offset, current variance, and preset current measurement specifications.

[0081] Here, the preset voltage measurement specifications may include the maximum measurable voltage of the voltage measurement unit and the voltage measurement error of the voltage measurement unit. For example, the maximum measurable voltage of the voltage measurement unit may be 5V, and the voltage measurement error of the voltage measurement unit may be 5%.

[0082] Similarly, the preset current measurement specifications may include the maximum current that can be measured by the current measurement unit and the current measurement error of the current measurement unit. For example, the maximum current that can be measured by the current measurement unit may be 100A, and the current measurement error of the current measurement unit may be 5%.

[0083] The offset noise matrix can be configured as a matrix that includes a first offset component and a second offset component.

[0084] For example, the offset noise matrix can be a matrix with the following structure.

[0085]

[0086] Here, W can be the offset noise matrix, w1 can be the first offset component, and w2 can be the second offset component.

[0087] Specifically, the system noise calculation unit 120 can be configured to calculate a first offset component based on current offset and current measurement specifications. Furthermore, the system noise calculation unit 120 can be configured to calculate a second offset component based on voltage offset and voltage measurement specifications. The specific equations for calculating the first and second offset components by the system noise calculation unit 120 will be described later.

[0088] The variance noise matrix can be configured as a matrix that includes a first variance component and a second variance component.

[0089] For example, the variance noise matrix can be a matrix with the following structure.

[0090]

[0091] Here, Q can be the variance noise matrix, q11 can be the first variance component, and q22 can be the second variance component. Typically, the values ​​of q12 and q21 can be 0.

[0092] The system noise calculation unit 120 can be configured to calculate a first variance component based on the current variance and current measurement specifications, and to calculate a second variance component based on the voltage variance and voltage measurement specifications. The specific equations used by the system noise calculation unit 120 to calculate the first and second variance components will be described later.

[0093] The system noise calculation unit 120 can be configured to calculate system noise based on the offset noise matrix and the variance noise matrix.

[0094] For example, system noise can be calculated by taking the dot product of the offset noise matrix and the variance noise matrix. The specific equations used by the system noise calculation unit 120 to calculate system noise will be described later.

[0095] In other words, by calculating the system noise used in estimating the SOC and SOH of the battery based on voltage offset, voltage variance, preset voltage measurement specifications, current offset, current variance, and preset current measurement specifications, the system noise caused by the voltage measurement unit and the current measurement unit can be more accurately reflected.

[0096] The battery state estimation unit 130 can be configured to estimate the battery state information by applying system noise to a preset recursive filter.

[0097] For example, a recursive filter may include a dual adaptive extended Kalman filter, which includes a first extended Kalman filter that predicts and corrects the SOC and SOC covariance of the battery, and a second extended Kalman filter that predicts and corrects the SOH and SOH covariance of the battery.

[0098] Specifically, the first extended Kalman filter can be configured to predict the SOC of the current cycle based on the SOC and offset noise matrix of the battery estimated in the previous cycle, predict the SOC covariance of the current cycle based on the SOC covariance of the battery estimated in the previous cycle and the system noise, and estimate the SOC and SOC covariance of the battery in the current cycle based on the predicted SOC, the predicted SOC covariance and the SOH predicted by the second extended Kalman filter.

[0099] Additionally, the second extended Kalman filter can be configured to predict the SOH of the current cycle based on the SOH of the battery estimated in the previous cycle, predict the SOH covariance of the current cycle based on the SOH covariance and variance noise matrix of the battery estimated in the previous cycle, and estimate the SOH and SOH covariance of the battery in the current cycle based on the predicted SOH, the predicted SOH covariance, and the SOC predicted by the first extended Kalman filter.

[0100] Since the details of the dual adaptive extended Kalman filter are described in Non-Patent Document 1, their detailed description will be omitted.

[0101] The battery state estimation unit 130 can estimate the battery state information by reflecting the system noise corresponding to the noise of the voltage measurement unit and the current measurement unit.

[0102] Therefore, the battery state estimation apparatus 100 according to the embodiments of the present disclosure has the advantage of estimating the current state of the battery more accurately by using system noise calculated based on voltage and current values ​​obtained during a predetermined time period, rather than simply applying arbitrary noise values ​​that follow a multivariate normal distribution.

[0103] Meanwhile, the offset and variance calculation unit 110, system noise calculation unit 120, and battery state estimation unit 130 included in the battery state estimation device 100 may optionally include application-specific integrated circuits (ASICs), another chipset, logic circuits, registers, communication modems, and data processing devices known in the art, to execute the various control logics disclosed below. Furthermore, when the control logic is implemented as software, the offset and variance calculation unit 110, system noise calculation unit 120, and battery state estimation unit 130 can be implemented as a set of program modules. In this case, the program modules can be stored in memory and executed by the offset and variance calculation unit 110, system noise calculation unit 120, and battery state estimation unit 130. The memory can be internal or external to the offset and variance calculation unit 110, system noise calculation unit 120, and battery state estimation unit 130, and can be connected to the offset and variance calculation unit 110, system noise calculation unit 120, and battery state estimation unit 130 by various well-known means.

[0104] Additionally, the battery state estimation device 100 may further include a storage unit 140. The storage unit 140 may store data necessary for the operation and function of each component of the battery state estimation device 100, data generated during the execution of operations or functions, etc. The type of storage unit 140 is not particularly limited, as long as it is a known information storage device capable of recording, erasing, updating, and retrieving data. As examples, the information storage device may include RAM, flash memory, ROM, EEPROM, registers, etc. Furthermore, the storage unit 140 may store program code that defines the processes that can be executed by the offset and variance calculation unit 110, the system noise calculation unit 120, and the battery state estimation unit 130.

[0105] For example, storage unit 140 can store the battery voltage and current values ​​obtained during a predetermined time period. Additionally, it can store the SOC and SOH of the previous cycle derived from the recursive filter.

[0106] The following text will describe in detail the calculation of the offset component, variance component, and system noise by the system noise calculation unit 120.

[0107] The system noise calculation unit 120 can be configured to calculate the first offset component using the following Equation 1.

[0108] [Equation 1]

[0109]

[0110] Here, w1 can be the first offset component, w 1_min It can be the preset minimum value of the first offset component, w 1_maxIt can be the preset maximum value of the first offset component, offset c It could be current offset, range c It can be the maximum measurable current of the current measurement specification, and accuracy. c It could be the current measurement error in the current measurement specification.

[0111] For example, suppose the minimum value of the first offset component (w) 1_min The value of the first offset component is 1, and the maximum value of the first offset component is (w). 1_max The value is 10, which allows for the measurement of the maximum current (range). c The current is 100A, and the current measurement accuracy is... c The figure is 5%.

[0112] If a current offset of 1A is generated during the predetermined time period, the first offset component can be calculated as 2.8. Specifically, the system noise calculation unit 120 can calculate the first offset component as 2.8 by calculating the equation “1+{1÷(100×0.05)×(10-1)}”.

[0113] The system noise calculation unit 120 can be configured to calculate the second offset component using the following equation 2.

[0114] [Equation 2]

[0115]

[0116] Here, w2 can be the second offset component, w 2_min It can be the preset minimum value of the second offset component, w 2_max It can be the preset maximum value of the second offset component, offset. v It can be voltage offset, range v It can be the maximum measurable voltage according to voltage measurement specifications, and accuracy. v It could be the voltage measurement error specified in the voltage measurement standard.

[0117] For example, suppose the minimum value of the second offset component (w) 2_min The value of the second offset component is 1, and the maximum value of the second offset component is (w). 2_max The value is 10, which allows for the measurement of the maximum voltage (range). v The voltage is 5V, and the voltage measurement accuracy is... v The figure is 5%.

[0118] If a voltage offset of 0.03V is generated during the predetermined time period, the second offset component can be calculated as 2.08. Specifically, the system noise calculation unit 120 can calculate the second offset component as 2.08 by calculating the equation “1+{0.03÷(5×0.05)×(10-1)}”.

[0119] The system noise calculation unit 120 can be configured to calculate the first variance component using the following equation 3.

[0120] [Equation 3]

[0121]

[0122] Here, q 11 It can be the first variance component, q 11_min It can be the preset minimum value of the first variance component, q 11_max It can be the preset maximum value of the first variance component, var c It can be the variance of the current, or the range. c It can be the maximum measurable current of the current measurement specification, and accuracy. c It could be the current measurement error in the current measurement specification.

[0123] For example, suppose the minimum value of the first variance component (q) 11_min The value is 0.01, and the maximum value of the first variance component (q) is... 11_max The value is 0.1, which allows for the measurement of the maximum current (range). c The current is 100A, and the current measurement accuracy is... c The figure is 5%.

[0124] If the current variance is 1 during the predetermined time period, the first variance component can be calculated as 0.0136. Specifically, the system noise calculation unit 120 can calculate the equation "0.01 + {1 ÷ (100 × 0.05)}". 2 The first variance component is calculated as 0.0136 using the formula ×(0.1-0.01)}”.

[0125] The system noise calculation unit 120 can be configured to calculate the second variance component using the following equation 4.

[0126] [Equation 4]

[0127]

[0128] Here, q 22 It can be the second variance component, q 22_min It can be the preset minimum value of the second variance component, q 22_maxIt can be the preset maximum value of the second variance component, var v It can be voltage variance, range v It can be the maximum measurable voltage according to voltage measurement specifications, and accuracy. v It could be the voltage measurement error specified in the voltage measurement standard.

[0129] For example, suppose the minimum value of the second variance component (q) 22_min The value is 0.01, and the maximum value of the second variance component (q) is... 22_max The value is 0.1, which allows for the measurement of the maximum voltage (range). v The voltage is 5V, and the voltage measurement accuracy is... v The figure is 5%.

[0130] If the voltage variance is 0.03 during the predetermined time period, the second variance component can be calculated as 0.0532. Specifically, the system noise calculation unit 120 can calculate the equation "0.01 + {0.03 ÷ (5 × 0.05)}". 2 The second variance component is calculated as 0.0532 using the formula ×(0.1-0.01)}”.

[0131] The system noise calculation unit 120 can be configured to calculate system noise by calculating the dot product of the offset noise matrix, the variance noise matrix, and the transpose of the offset noise matrix.

[0132] Specifically, the system noise calculation unit 120 can be configured to calculate the system noise using the following equation 5.

[0133] [Equation 5]

[0134]

[0135] here, W can be system noise, and W can be the offset noise matrix. T Q can be the transpose of the offset noise matrix, and Q can be the variance noise matrix.

[0136] For example, as in the previous embodiment, assume that the first offset component (w1) is 2.8, the second offset component (w2) is 2.08, and the first variance component (q) is... 11 The variance is 0.0136, and the second variance component (q) 22 The system noise calculation unit 120 can calculate the system noise as 0.33678848 by calculating the dot product of the offset noise matrix, the variance noise matrix, and the transpose of the offset noise matrix.

[0137] Since the battery state estimation apparatus 100 according to the embodiments of the present disclosure takes into account the noise of the voltage measurement unit and the current measurement unit to calculate the system noise (∑w), it has the advantage that the accuracy of the estimation of the battery state (SOC and SOH) using recursive filters can be greatly improved.

[0138] The following will describe in detail an embodiment in which the battery state estimation unit 130 applies the offset noise matrix, variance noise matrix and system noise to the recursive filter.

[0139] First, the dual adaptive extended Kalman filter comprises a first extended Kalman filter and a second extended Kalman filter. The first extended Kalman filter is a recursive filter that predicts the SOC and SOC covariance in the time update step and corrects the predicted SOC and predicted SOC covariance in the measurement update step. The second extended Kalman filter is a recursive filter that predicts the SOH and SOH covariance in the time update step and corrects the predicted SOH and predicted SOH covariance in the measurement update step.

[0140] Furthermore, in the following text, “^” means the estimated value, “-” means the predicted value in the time update step, and “+” means the corrected value in the measurement update step.

[0141] However, the parameters (P, x, u, A, θ, and k) included in Equations 6 through 8 below are defined in Non-Patent Document 1, and these parameters are widely used in Extended Kalman Filters or Dual Adaptive Extended Kalman Filters. Therefore, a detailed description of the parameters (P, x, u, A, θ, and k) is omitted below.

[0142] The battery state estimation unit 130 can use the following Equation 6 to predict the SOH covariance of the battery in the second extended Kalman filter.

[0143] [Equation 6]

[0144]

[0145] Here, Equation 6 corresponds to Equation 12 in Non-Patent Document 1, and It is the variance noise matrix calculated by the system noise calculation unit 120 at time point k.

[0146] The battery state estimation unit 130 can use the variance noise matrix calculated by the system noise calculation unit 120 ( The SOH covariance determined by the second extended Kalman filter at time point k (the SOH covariance that has undergone time and measurement updates at time point k) is compared with the SOH covariance determined by the second extended Kalman filter at time point k. The sum of these values ​​is used to predict the SOH covariance at time point k+1. ).

[0147] Subsequently, the SOH covariance predicted by the battery state estimation unit 130 can be corrected according to Equation 16 of Non-Patent Document 1. Specifically, the predicted SOH covariance can be corrected according to Equation 16 of Non-Patent Literature 1. To determine the SOH covariance at time k+1 ( ).

[0148] In other words, since the battery state estimation unit 130 further considers the variance noise matrix during the time update process of the second extended Kalman filter ( To predict the covariance of SOH () Therefore, the noise of the voltage measurement unit and the current measurement unit can be reflected in the predicted SOH covariance ( )middle.

[0149] Therefore, referring to Equation 16 of Non-Patent Literature 1, based on the predicted SOH covariance ( Corrected SOH ( () can represent the SOH of the battery more accurately.

[0150] The battery state estimation unit 130 can predict the battery's SOC using the following equation 7 in the first extended Kalman filter.

[0151] [Equation 7]

[0152]

[0153] Here, Equation 7 corresponds to Equation 13 in Non-Patent Document 1, and It is the offset noise matrix calculated by the system noise calculation unit 120 at time point k.

[0154] The battery state estimation unit 130 can use the offset noise matrix calculated by the system noise calculation unit 120 ( The SOC determined by the first extended Kalman filter at time point k (which has already undergone time and measurement updates at time point k) and the SOC determined by the first extended Kalman filter at time point k. The sum of these two numbers is used to predict the SOC at time point k+1. ).

[0155] Then, the SOC predicted by the battery state estimation unit 130 can be corrected according to Equation 14 of Non-Patent Document 1. Specifically, the predicted SOC can be corrected according to Equation 14 of Non-Patent Document 1. ) to determine the SOC at time point k+1 ( ).

[0156] In other words, since the battery state estimation unit 130 further considers the offset noise matrix during the time update process of the first extended Kalman filter ( To predict SOC ( Therefore, the noise of the voltage measurement unit and the current measurement unit can be reflected in the predicted SOC (State of Charge). )middle.

[0157] Additionally, referring to Equation 14 of Non-Patent Literature 1, the predicted SOC covariance described later can be considered ( ) to correct the predicted SOC ( Therefore, the corrected SOC ( This can represent the battery's SOC more accurately.

[0158] The battery state estimation unit 130 can use the following equation 8 to predict the SOC covariance of the battery in the first extended Kalman filter.

[0159] [Equation 8]

[0160]

[0161] Here, Equation 8 corresponds to Equation 13 in Non-Patent Document 1, and The system noise is calculated by the system noise calculation unit 120 at time point k.

[0162] The battery state estimation unit 130 can use the system noise calculated by the system noise calculation unit 120 to estimate the system noise ( The SOC covariance determined by the first extended Kalman filter at time point k (the SOC covariance that has undergone time and measurement updates at time point k) is compared with the SOC covariance determined by the first extended Kalman filter at time point k. The sum of these values ​​is used to predict the SOC covariance at time point k+1. ).

[0163] Then, the SOC covariance predicted by the battery state estimation unit 130 can be corrected according to Equation 14 of Non-Patent Document 1. Specifically, the predicted SOC covariance can be corrected according to Equation 14 of Non-Patent Literature 1. To determine the SOC covariance at time k+1 ( ).

[0164] In other words, since the battery state estimation unit 130 further considers system noise during the time update process of the first extended Kalman filter ( To predict SOC covariance ( Therefore, the noise of the voltage measurement unit and the current measurement unit can be reflected in the predicted SOC covariance ( )middle.

[0165] Therefore, referring to Equation 14 of Non-Patent Literature 1, based on the predicted SOC covariance ( ) Corrected SOC ( This can represent the battery's SOC more accurately.

[0166] The battery state estimation device 100 according to this disclosure can be applied to a battery management system (BMS). That is, the BMS according to this disclosure may include the battery state estimation device 100 described above. In this configuration, at least some of the components of the battery state estimation device 100 can be implemented by supplementing or adding the functionality of components included in a conventional BMS. For example, each component of the battery state estimation device 100 can be implemented as a component of the BMS.

[0167] Furthermore, the battery state estimation device 100 according to this disclosure can be provided to a battery pack. That is, a battery pack according to this disclosure may include the aforementioned battery state estimation device 100 and one or more individual battery cells. In addition, the battery pack may further include electrical components (relays, fuses, etc.) and a housing.

[0168] Figure 3 This is a diagram schematically illustrating an exemplary configuration of a battery pack according to another embodiment of the present disclosure.

[0169] refer to Figure 3 The battery pack 1 may include a battery B, a voltage measurement unit 10, a current measurement unit 20, and a battery state estimation device 100.

[0170] The positive terminal of battery B can be connected to the positive terminal P+ of battery pack 1, and the negative terminal of battery B can be connected to the negative terminal P- of battery pack 1.

[0171] The voltage measurement unit 10 can be connected to the first sensing line SL1 and the second sensing line SL2.

[0172] Specifically, the voltage measuring unit 10 can be connected to the positive terminal of the battery B via a first sensing line SL1, and can be connected to the negative terminal of the battery B via a second sensing line SL2. The voltage measuring unit 10 can measure the voltage of the battery B based on the voltage measured at each of the first sensing line SL1 and the second sensing line SL2.

[0173] The current measurement unit 20 can be connected to the third sensing line SL3.

[0174] Additionally, the current measuring unit 20 can be connected to the current measuring unit A via the third sensing line SL3. For example, the current measuring unit A can be an ammeter or a shunt resistor installed on the charging and discharging paths of the battery B and capable of measuring the current in those paths. The current measuring unit 20 can measure the charging current of the battery B via the third sensing line SL3.

[0175] The offset and variance calculation unit 110 can receive the voltage value of battery B from the voltage measurement unit 10. Furthermore, the offset and variance calculation unit 110 can receive the current value of battery B from the current measurement unit 20.

[0176] Additionally, in calculating the first offset component (w1) of Equation 1 and the first variance component (q) of Equation 3... 11 Among the current measurement specifications, the maximum measurable current (range) is... c The current measurement unit 20 can be the maximum current value that can be measured. For example, the maximum current value that can be measured by the current measurement unit 20 can be 100A. Furthermore, in the current measurement specifications, the current measurement accuracy... c This could be the current measurement error of the current measuring unit 20. For example, the current measurement error of the current measuring unit 20 could be 5%.

[0177] Additionally, in calculating the second offset component (w2) of Equation 2 and the second variance component (q) of Equation 4... 22 In the voltage measurement specifications, the maximum measurable voltage (range) is... v The voltage value that can be measured by the voltage measuring unit 10 can be 5V. For example, the maximum voltage value that can be measured by the voltage measuring unit 10 can be 5V. Furthermore, in the voltage measurement specifications, the voltage measurement accuracy... v This could be the measurement error of the voltage measuring unit 10. For example, the voltage measurement error of the voltage measuring unit 10 could be 5%.

[0178] refer to Figure 3 The voltage value measured by the voltage measurement unit 10 and the current value measured by the current measurement unit 20 can be directly transmitted to the offset and variance calculation unit 110 or stored in the storage unit 140. When the voltage and current values ​​are stored in the storage unit 140, the offset and variance calculation unit 110 can access the storage unit 140 to obtain the voltage and current values ​​for a predetermined time period.

[0179] An energy storage system (ESS) according to another embodiment of the present disclosure may include a battery state estimation device 100 according to an embodiment of the present disclosure.

[0180] An energy storage system is a storage system that includes multiple battery racks, and each battery rack can include multiple battery packs. A battery pack can consist of multiple battery modules, and each battery module can consist of multiple individual battery cells.

[0181] A battery state estimation device 100 can be provided for each battery rack and / or battery pack that constitutes the energy storage system.

[0182] For example, a battery state estimation device 100 can be provided for each battery rack constituting the energy storage system, and the battery state estimation device 100 can calculate the system noise for the corresponding battery rack. In addition, the battery state estimation device 100 can use a recursive filter and the calculated system noise (typically applied to multiple battery packs included in the battery rack) to estimate the state of the battery packs.

[0183] As another example, a battery state estimation device 100 can be provided for each battery pack constituting the battery rack, and the battery state estimation device 100 calculates the system noise for the corresponding battery pack. Alternatively, the battery state estimation device 100 can use a recursive filter and the calculated system noise (applied to each battery pack) to estimate the state of the battery pack.

[0184] Preferably, when the battery state estimation device 100 calculates the system noise of each battery cell, which is the smallest unit constituting the energy storage system, the state of the battery cell, battery module, battery pack, and battery rack can be estimated more accurately. However, since it is practically impossible to have a battery state estimation device 100 in every battery cell constituting the energy storage system, a battery state estimation device 100 can be provided in each battery rack or each battery pack to estimate the state of the battery pack.

[0185] In the foregoing, due to the practical problem that it is impossible to equip all battery cells constituting the energy storage system with the battery state estimation device 100, embodiments in which the battery state estimation device 100 is arranged in the battery rack and / or battery pack have been described. However, it should be noted that embodiments in which the battery state estimation device 100 is arranged for each battery cell or each battery module are not limited to this description.

[0186] Figure 4 This is a diagram schematically illustrating a battery state estimation method according to yet another embodiment of the present disclosure.

[0187] Preferably, each step of the battery state estimation method can be performed by the battery state estimation device 100. In the following, content overlapping with the previously described content will be briefly described or omitted.

[0188] refer to Figure 4The battery state estimation method may include a voltage and current acquisition step (S100), an offset and variance calculation step (S200), a noise matrix calculation step (S300), a system noise calculation step (S400), and a battery state information estimation step (S500).

[0189] The voltage and current acquisition step (S100) is the step of acquiring the voltage and current values ​​of the battery, and can be performed by the offset and variance calculation unit 110.

[0190] Here, the voltage and current obtained by the offset and variance calculation unit 110 can be the voltage and current values ​​of the battery under no-load conditions.

[0191] For example, in Figure 3 In one embodiment, the offset and variance calculation unit 110 can receive the voltage value of battery B from the voltage measurement unit 10 and the current value of battery B from the current measurement unit 20.

[0192] As another example, in Figure 3 In this embodiment, the storage unit 140 can receive and store the voltage value of battery B from the voltage measurement unit 10, and receive and store the current value of battery B from the current measurement unit 20. The offset and variance calculation unit 110 can access the storage unit 140 to obtain the voltage and current values ​​of battery B.

[0193] The offset and variance calculation step (S200) is a step of calculating voltage offset and voltage variance based on the battery voltage value obtained during a predetermined time period, and calculating current offset and current variance based on the battery current value obtained during the predetermined time period, and can be executed by the offset and variance calculation unit 110.

[0194] For example, in Figure 3 In this embodiment, the offset and variance calculation unit 110 can calculate the offset of the voltage measurement unit 10 based on multiple voltage values ​​measured by the voltage measurement unit 10 within a predetermined time period. Furthermore, the offset and variance calculation unit 110 can calculate the maximum or average value of the multiple calculated offsets as the voltage offset. Additionally, the offset and variance calculation unit 110 can calculate the variance of the multiple calculated offsets as the voltage variance.

[0195] In addition, Figure 3 In this embodiment, the offset and variance calculation unit 110 can calculate the offset of the current measurement unit 20 based on multiple current values ​​measured by the current measurement unit 20 during a predetermined time period. Furthermore, the offset and variance calculation unit 110 can calculate the maximum or average value among the multiple calculated offsets as the current offset. Additionally, the offset and variance calculation unit 110 can calculate the variance of the multiple offsets as the current variance.

[0196] The noise matrix calculation step (S300) is a step to calculate the offset noise matrix and the variance noise matrix based on voltage offset, voltage variance, preset voltage measurement specifications, current offset, current variance, and preset current measurement specifications, and can be executed by the system noise calculation unit 120.

[0197] For example, the system noise calculation unit 120 can calculate the first offset component (w1) based on current offset, current variance, and current measurement specifications according to Equation 1. Furthermore, the system noise calculation unit 120 can calculate the second offset component (w2) based on voltage offset, voltage variance, and voltage measurement specifications according to Equation 2. Additionally, the system noise calculation unit 120 can determine an offset noise matrix (W) that includes the first offset component (w1) and the second offset component (w2).

[0198] In addition, the system noise calculation unit 120 can calculate the first variance component (q) based on current offset, current variance, and current measurement specifications according to Equation 3. 11 Additionally, the system noise calculation unit 120 can calculate the second variance component (q) based on voltage offset, voltage variance, and voltage measurement specifications according to Equation 4. 22 Furthermore, the system noise calculation unit 120 can determine the first variance component (q). 11 ) and the second variance component (q) 22 The variance noise matrix (Q) of ).

[0199] The system noise calculation step (S400) is a step of calculating system noise based on the offset noise matrix and the variance noise matrix, and can be executed by the system noise calculation unit 120.

[0200] For example, the system noise calculation unit 120 can calculate the offset noise matrix (W), the variance noise matrix (Q), and the transpose matrix (W) of the offset noise matrix (W) according to Equation 5. T The system noise is calculated by the dot product of ( ). ).

[0201] The battery state information estimation step (S500) is a step of estimating the battery state information by applying system noise to a preset recursive filter, and can be executed by the battery state estimation unit 130.

[0202] For example, the battery state estimation unit 130 can further add the offset noise matrix (W) to the SOC prediction process of the first extended Kalman filter.

[0203] In addition, the battery state estimation unit 130 can further reduce system noise ( This is added to the SOC covariance prediction process of the first extended Kalman filter.

[0204] In addition, the battery state estimation unit 130 can further add the variance noise matrix (Q) to the SOH covariance prediction process of the second extended Kalman filter.

[0205] The battery state estimation unit 130 has the following advantages: by using the offset noise matrix (W), variance noise matrix (Q), and system noise ( This allows for a more accurate estimation of the battery's SOC and SOH.

[0206] The embodiments of this disclosure described above can be implemented not only by apparatus and methods, but also by a program that implements functions corresponding to the configuration of the embodiments of this disclosure, or a recording medium on which the program is recorded. Based on the description of the embodiments above, those skilled in the art can readily implement the program or recording medium.

[0207] This disclosure has been described in detail. However, it should be understood that while the detailed description and specific examples indicate preferred embodiments of this disclosure, they are given by way of illustration only, as various variations and modifications within the scope of this disclosure will become apparent to those skilled in the art based on this detailed description.

[0208] Furthermore, since those skilled in the art can replace, modify, and change the above disclosure in various ways without departing from the technical concept of the disclosure, the disclosure is not limited to the above embodiments and drawings, and all or some of the embodiments can be selectively combined to achieve various modifications.

[0209] (Explanation of the labels in the attached diagram)

[0210] 1: Battery pack

[0211] 10: Voltage Measurement Unit

[0212] 20: Unit of Current Measurement

[0213] 100: Battery State Estimation Device

[0214] 110: Offset and Variance Calculation Unit

[0215] 120: System noise calculation unit

[0216] 130: Battery State Estimation Unit

[0217] 140: Storage unit

Claims

1. A battery state estimation device, comprising: An offset and variance calculation unit is configured to calculate voltage offset and voltage variance based on the battery voltage value obtained during a predetermined time period, and to calculate current offset and current variance based on the battery current value obtained during the predetermined time period. The system noise calculation unit is configured to calculate a first offset component using Equation 1 below, calculate a second offset component using Equation 2 below, calculate a first variance component using Equation 3 below, calculate a second variance component using Equation 4 below, calculate an offset noise matrix based on the first offset component and the second offset component, calculate a variance noise matrix based on the first variance component and the second variance component, and calculate system noise based on the offset noise matrix and the variance noise matrix. [Equation 1] Where w1 is the first offset component, w 1_min It is the preset minimum value of the first offset component, w 1_max It is the preset maximum value of the first offset component, offset c It is the current offset, range c It is the maximum measurable current of the preset current measurement specifications, and its accuracy is... c It is the current measurement error of the preset current measurement specification; [Equation 2] Where w2 is the second offset component, w 2_min It is the preset minimum value of the second offset component, w 2_max It is the preset maximum value of the second offset component, offset v It is the voltage offset, range v It is the maximum measurable voltage according to the preset voltage measurement specifications, and its accuracy is... v It is the voltage measurement error of the preset voltage measurement specification; [Equation 3] Where, q 11 It is the first variance component, q 11_min It is the preset minimum value of the first variance component, q 11_max It is the preset maximum value of the first variance component, var c It is the current variance, range c It is the maximum measurable current of the preset current measurement specification, and its accuracy is... c It is the current measurement error of the preset current measurement specification; [Equation 4] Where, q 22 It is the second variance component, q 22_min It is the preset minimum value of the second variance component, q 22_max It is the preset maximum value of the second variance component, var v It is the voltage variance, range v It is the maximum measurable voltage of the preset voltage measurement specification, and its accuracy is... v It is the voltage measurement error of the preset voltage measurement specification; and A battery state estimation unit is configured to estimate the state information of the battery by applying the system noise to a preset recursive filter.

2. The battery state estimation device according to claim 1, in, The system noise calculation unit is configured to calculate the system noise by calculating the dot product of the offset noise matrix, the variance noise matrix, and the transpose of the offset noise matrix.

3. The battery state estimation device according to claim 2, in, The system noise calculation unit is configured to calculate the system noise using the following Equation 5. [Equation 5] in, W is the system noise, and W is the offset noise matrix. T is the transpose of the offset noise matrix, and Q is the variance noise matrix.

4. The battery state estimation device according to claim 1, in, The recursive filter is configured as a dual adaptive extended Kalman filter, which includes a first extended Kalman filter that predicts and corrects the SOC and SOC covariance of the battery, and a second extended Kalman filter that predicts and corrects the SOH and SOH covariance of the battery.

5. The battery state estimation device according to claim 4, in, The first extended Kalman filter is configured to predict the SOC of the current cycle based on the SOC of the battery estimated in the previous cycle and the offset noise matrix, predict the SOC covariance of the current cycle based on the SOC covariance of the battery estimated in the previous cycle and the system noise, and estimate the SOC and SOC covariance of the battery in the current cycle based on the predicted SOC, the predicted SOC covariance and the SOH predicted by the second extended Kalman filter.

6. The battery state estimation device according to claim 4, in, The second extended Kalman filter is configured to predict the SOH of the current cycle based on the SOH of the battery estimated in the previous cycle, predict the SOH covariance of the current cycle based on the SOH covariance of the battery estimated in the previous cycle and the variance noise matrix, and estimate the SOH and SOH covariance of the battery in the current cycle based on the predicted SOH, the predicted SOH covariance and the SOC predicted by the first extended Kalman filter.

7. A battery pack comprising a battery state estimation device according to any one of claims 1 to 6.

8. An energy storage system comprising a battery state estimation device according to any one of claims 1 to 6.

9. A battery state estimation method, comprising: The voltage and current acquisition steps obtain the battery's voltage and current values; The offset and variance calculation steps calculate voltage offset and voltage variance based on the voltage values ​​of the battery obtained during a predetermined time period, and calculate current offset and current variance based on the current values ​​of the battery obtained during the predetermined time period. The noise matrix calculation steps are as follows: use Equation 1 below to calculate the first offset component, use Equation 2 below to calculate the second offset component, use Equation 3 below to calculate the first variance component, use Equation 4 below to calculate the second variance component, calculate the offset noise matrix based on the first offset component and the second offset component, and calculate the variance noise matrix based on the first variance component and the second variance component. [Equation 1] Where w1 is the first offset component, w 1_min It is the preset minimum value of the first offset component, w 1_max It is the preset maximum value of the first offset component, offset c It is the current offset, range c It is the maximum measurable current of the preset current measurement specifications, and its accuracy is... c It is the current measurement error of the preset current measurement specification; [Equation 2] Where w2 is the second offset component, w 2_min It is the preset minimum value of the second offset component, w 2_max It is the preset maximum value of the second offset component, offset v It is the voltage offset, range v It is the maximum measurable voltage according to the preset voltage measurement specifications, and its accuracy is... v It is the voltage measurement error of the preset voltage measurement specification; [Equation 3] Where, q 11 It is the first variance component, q 11_min It is the preset minimum value of the first variance component, q 11_max It is the preset maximum value of the first variance component, var c It is the current variance, range c It is the maximum measurable current of the preset current measurement specification, and its accuracy is... c It is the current measurement error of the preset current measurement specification; [Equation 4] Where, q 22 It is the second variance component, q 22_min It is the preset minimum value of the second variance component, q 22_max It is the preset maximum value of the second variance component, var v It is the voltage variance, range v It is the maximum measurable voltage of the preset voltage measurement specification, and its accuracy is... v It is the voltage measurement error of the preset voltage measurement specification; The system noise calculation steps involve calculating the system noise based on the offset noise matrix and the variance noise matrix; and The battery state information estimation step estimates the battery state information by applying the system noise to a preset recursive filter.

Images