Patent Description:
Recently, there has been a rapid increase in the demand for portable electronic products such as laptop computers, video cameras and mobile phones, and with the extensive development of electric vehicles, accumulators for energy storage, robots and satellites, many studies are being made on high performance batteries that can be recharged repeatedly.

Currently, commercially available batteries include nickel-cadmium batteries, nickel-hydrogen batteries, nickel-zinc batteries, lithium batteries and the like, and among them, lithium batteries have little or no memory effect, and thus they are gaining more attention than nickel-based batteries for their advantages that recharging can be done whenever it is convenient, the self-discharge rate is very low and the energy density is high.

A battery cell gradually degrades over time due to the repeated charging and discharging. As the battery cell degrades, the internal chemical state of the battery cell also changes. However, the internal variables (e.g., electrical conductivity of the positive electrode active material) indicating the internal chemical state the battery cell cannot be observed outside the battery cell.

Meanwhile, the internal chemical state of the battery cell causes a change in external variables (e.g., voltage and temperature of the battery cell) that can be observed outside the battery cell. Accordingly, there are attempts to estimate the internal variables based on the external variables.

However, some of the internal variables and some of the external variables may have a very low correlation. Thus, without considering the correlation between the internal variables and the external variables, the internal variables estimated using the external variables may be greatly different from the actual internal chemical state of the battery cell. Document: <NPL>, relates to a method of mapping internal variables to external variables in a battery system.

The present disclosure is designed to solve the above-described problem, and therefore the present disclosure is directed to providing a battery management apparatus, a battery management method, and a battery pack that analyzes a correlation between internal variables and external variables of a battery cell.

The present disclosure is further directed to providing a battery management apparatus, a battery management method and a battery pack, in which only external variables having at least a certain level of correlation with each internal variable are used in the modeling of a sub-multilayer perceptron necessary for estimation of each internal variable.

These and other objects and advantages of the present disclosure may be understood by the following description and will be apparent from the embodiments of the present disclosure. In addition, it will be readily understood that the objects and advantages of the present disclosure may be realized by the means set forth in the appended claims and a combination thereof.

A battery management apparatus according to claim <NUM> is provided in a first aspect.

The memory unit may be further configured to store a main multilayer perceptron that defines a correspondence relationship between the first to mth external variables and the first to nth internal variables. The control unit may be configured to acquire first to nth output data sets from first to nth output nodes included in an output layer of the main multilayer perceptron by providing the first to mth input data sets to first to mth input nodes included in an input layer of the main multilayer perceptron. Each of the first to nth output data sets may include the same number of result values as the predetermined number. The control unit may be configured to determine first to nth error factors based on the first to nth output data sets and the first to nth desired data sets. The control unit may be configured to determine first to nth reference values by comparing each of the first to nth error factors with a threshold error factor.

The control unit may be configured to determine a jth error factor to be equal to an error ratio of the jth output data set to the jth desired data set when j is an integer of <NUM> to n.

The control unit may be configured to set the jth reference value to be equal to a first predetermined value when the jth error factor is smaller than the threshold error factor.

The control unit may be configured to set the jth reference value to be equal to a second predetermined value when the jth error factor is equal to or larger than the threshold error factor. The second predetermined value may be smaller than the first predetermined value.

The control unit may be configured to determine whether to set an ith external variable among the first to mth external variables as a valid external variable for the jth internal variable based on a ith input data set, a jth desired data set and a jth reference value, when i is an integer of <NUM> to m and j is an integer of <NUM> to n. The control unit may be configured to learn a sub-multilayer perceptron associated with the jth internal variable using the ith input data set as training data when the ith external variable is set as the valid external variable for the jth internal variable.

The control unit may be configured to determine a multiple correlation coefficient between the ith input data set and the jth desired data set. The control unit may be configured to set the ith external variable as the valid external variable for the jth internal variable when an absolute value of the multiple correlation coefficient is larger than the jth reference value.

The control unit may be configured to set the ith external variable as an invalid external variable for the jth internal variable when the absolute value of the multiple correlation coefficient is equal to or less than the jth reference value.

A battery pack according to another aspect of the present disclosure includes the battery management apparatus.

A battery management method according to still another aspect of the present disclosure is provided in claim <NUM>.

According to at least one of the embodiments of the present disclosure, it is possible to analyze a correlation between internal variables and external variables of a battery cell.

According to at least one of the embodiments of the present disclosure, when a correlation between two external variables is equal to or larger than a predetermined level, only an observational data set for one of the two external variables is extracted, and the extracted observational data set may be used in the modeling of a sub-multilayer perceptron.

According to at least one of the embodiments of the present disclosure, only external variables having at least a certain level of correlation with each internal variable may be used in the modeling of a sub-multilayer perceptron necessary for estimation of each internal variable.

The effects of the present disclosure are not limited to the effects mentioned above, and these and other effects will be clearly understood by those skilled in the art from the appended claims.

Therefore, the embodiments described herein and illustrations shown in the drawings are just a most preferred embodiment of the present disclosure, but not intended to fully describe the technical aspects of the present disclosure, so it should be understood that a variety of other equivalents and modifications could have been made thereto at the time that the application was filed.

Unless the context clearly indicates otherwise, it will be understood that the term "comprises" when used in this specification, specifies the presence of stated elements, but does not preclude the presence or addition of one or more other elements. Additionally, the term "control unit" as used herein refers to a processing unit of at least one function or operation, and this may be implemented by hardware and software either alone or in combination.

<FIG> is a diagram exemplarily showing a configuration of a battery pack <NUM> including a battery management apparatus <NUM> according to the present disclosure, <FIG> is a diagram exemplarily showing a main multilayer perceptron used by the battery management apparatus <NUM> of <FIG>, and <FIG> is a diagram exemplarily showing a sub-multilayer perceptron.

Referring to <FIG>, the battery pack <NUM> includes a battery cell <NUM>, a switch <NUM> and the battery management apparatus <NUM>.

The battery pack <NUM> is mounted on an electricity-powered device such as an electric vehicle to supply electrical energy required to drive the electricity-powered device. The battery management apparatus <NUM> is provided to be electrically connected to positive and negative terminals of the battery cell <NUM>.

The battery cell <NUM> may be a lithium ion cell. The battery cell <NUM> may include any type that can be repeatedly charged and discharged, and is not limited to the lithium ion cell.

The battery cell <NUM> may be electrically coupled to an external device through power terminals (+, -) of the battery pack <NUM>. The external device may be, for example, an electrical load (e.g., a motor), a direct current (DC)-alternating current (AC) inverter and a charger of the electric vehicle.

The switch <NUM> is installed on a current path connecting the positive terminal of the battery cell <NUM> to the power terminal (+) or a current path connecting the negative terminal of the battery cell <NUM> to the power terminal (-). While the switch <NUM> is in an open operating state, the battery cell <NUM> stops charging and discharging. While the switch <NUM> is in a closed operating state, the battery cell <NUM> is allowed to charge and discharge.

The battery management apparatus <NUM> includes an interface unit <NUM>, a memory unit <NUM> and a control unit <NUM>. The battery management apparatus <NUM> may further include a sensing unit <NUM>.

The sensing unit <NUM> includes a voltage sensor <NUM>, a current sensor <NUM> and a temperature sensor <NUM>. The voltage sensor <NUM> is configured to measure a voltage across the battery cell <NUM>. The current sensor <NUM> is configured to measure an electric current flowing through the battery cell <NUM>. The temperature sensor <NUM> is configured to measure a temperature of the battery cell <NUM>. The sensing unit <NUM> may transmit sensing data indicating the measured voltage, the measured current, and the measured temperature to the control unit <NUM>.

The interface unit <NUM> may be coupled to the external device to enable communication between. The external device may be, for example, a charging system, an electrical load and a mobile device. The interface unit <NUM> is configured to support wired communication or wireless communication between the control unit <NUM> and the external device. The wired communication may be, for example, controller area network (CAN) communication, and the wireless communication may be, for example, ZigBee or Bluetooth communication. The interface unit <NUM> may include an output device such as a display or a speaker to provide the results of each operation performed by the control unit <NUM> in a form that can be recognized by a user. The interface unit <NUM> may include an input device such as a mouse and a keyboard to receive data from the user.

The memory unit <NUM> is operably coupled to at least one of the interface unit <NUM>, the control unit <NUM> or the sensing unit <NUM>. The memory unit <NUM> may store the results of each operation performed by the control unit <NUM>. The memory unit <NUM> may include, for example, at least one type of storage medium of flash memory type, hard disk type, Solid State Disk (SSD) type, Silicon Disk Drive (SDD) type, multimedia card micro type, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM) or programmable read-only memory (PROM).

The memory unit <NUM> is configured to store various data required to estimate the internal chemical state of the battery cell <NUM>. Specifically, the memory unit <NUM> stores a first number of observational data sets and a second number of desired data sets. For example, information including the first to pth observational data sets and the first to nth desired data sets is received from the external device by the interface unit <NUM>. The memory unit <NUM> may further store the main multilayer perceptron. Hereinafter, it is assumed that p indicates the first number and is an integer of <NUM> or greater, and n indicates the second number and is an integer of <NUM> or greater.

The first to pth observational data sets are each associated with p external variables that are observable outside the battery cell <NUM>. Each observational data set includes a predetermined third number (e.g., <NUM>) of input values. For example, the third number of input values for one of the p external variables are included in the qth observational data set.

The control unit <NUM> is operably coupled to at least one of the interface unit <NUM>, the memory unit <NUM> or the sensing unit <NUM>. The control unit <NUM> may be implemented in hardware using at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), microprocessors or electrical units for performing other functions.

The control unit <NUM> extracts m observational data sets from the first to pth observational data sets through data filtering, and sets the m observational data sets as first to mth input data sets according to the extraction order. m is an integer of <NUM> or greater and p or smaller. A process of extracting the m observational data sets will be described below with reference to <FIG>.

In the specification, Xi denotes the ith input data set associated with the ith external variable, and Xi(k) denotes the kth input value of the ith input data set.

For example, when m = <NUM>, the first to sixteenth external variables may be defined as follows.

The first external variable may indicate the period of time from the time point when the state of charge (SOC) of the battery cell <NUM> is equal to a first SOC (e.g., <NUM>%) to the time point when the voltage of the battery cell <NUM> reaches a first voltage (e.g., <NUM> V) by a first test of discharging the battery cell <NUM> with a current of a first current rate (e.g., <NUM>/<NUM> C) at a first temperature.

The second external variable may indicate the voltage of the battery cell <NUM> at a time point when a second test is performed for a first reference time (e.g., <NUM> sec), the second test of discharging the battery cell <NUM> with a current of a second current rate (e.g., <NUM> A) at a second temperature from the time point when the SOC of the battery cell <NUM> is equal to a second SOC (e.g., <NUM>%).

The third external variable may indicate the voltage of the battery cell <NUM> at the time point when the second test is performed for a second reference time (e.g., <NUM> sec) that is longer than the first reference time.

The fourth external variable may indicate the voltage of the battery cell <NUM> at the time point when the second test is performed for a third reference time (e.g., <NUM> sec) that is longer than the second reference time.

The fifth external variable may indicate the voltage of the battery cell <NUM> at the time point when the second test is performed for a fourth reference time (e.g., <NUM> sec) that is longer than the third reference time.

The sixth external variable may include the period of time from the time point when the SOC of the battery cell <NUM> is equal to a third SOC (e.g., <NUM>%) to the time point when the voltage of the battery cell <NUM> reaches a second voltage (e.g., <NUM> V) by a third test of discharging the battery cell <NUM> with a current of a third current rate (e.g., <NUM> C) at a third temperature.

The seventh external variable may indicate the period of time until the time point when the voltage of the battery cell <NUM> reaches a third voltage (e.g., <NUM> V) that is lower than the second voltage by the third test.

The eighth external variable may represent the time taken for the voltage of the battery cell <NUM> to reduce from the second voltage to the third voltage by the third test.

The ninth external variable may indicate the voltage of the battery cell <NUM> at the time point when a fourth test of discharging the battery cell <NUM> with a current of a fourth current rate (e.g., <NUM> C) at a fourth temperature is performed for a fifth reference time (e.g., <NUM> sec) from the time point when the SOC of the battery cell <NUM> is equal to a fourth SOC (e.g., <NUM> %).

The tenth external variable may indicate the voltage of the battery cell <NUM> at the time point when the fourth test is performed for a sixth reference time (e.g., <NUM> sec) that is longer than the fifth reference time.

The eleventh external variable may indicate the voltage of the battery cell <NUM> at the time point when the fourth test is performed for a seventh reference time (e.g., <NUM> sec) that is longer than the sixth reference time.

The twelfth external variable may indicate the voltage of the battery cell <NUM> at the time point when the fourth test is performed for an eighth reference time (e.g., <NUM> sec) that is longer than the seventh reference time.

The thirteenth external variable may indicate the voltage of the battery cell <NUM> at the time point when the fourth test is performed for a ninth reference time (e.g., <NUM> sec) that is longer than the eighth reference time.

The fourteenth external variable may indicate a ratio between a voltage change of the battery cell <NUM> during a first period (e.g., <NUM> to <NUM> sec) and a voltage change of the battery cell <NUM> during a second period (e.g., <NUM> to <NUM> sec) by a fifth test of charging the battery cell <NUM> with a current of a fifth current rate (e.g., <NUM> C) at a fifth temperature from the time point when the SOC of the battery cell <NUM> is equal to a fifth state (e.g., <NUM>%).

The fifteenth external variable may indicate the voltage of the first peak on a differential capacity curve of the battery cell <NUM> obtained by a sixth test of charging the battery cell <NUM> with a current of a sixth current rate (e.g., <NUM> C) at a sixth temperature from the time point when the SOC of the battery cell <NUM> is equal to a sixth SOC (e.g., <NUM>%). When V, dV and dQ are the voltage of the battery cell <NUM>, the voltage change of the battery cell <NUM>, and the capacity change of the battery cell <NUM>, respectively, the differential capacity curve shows a correspondence relationship between V and dQ/dV. The differential capacity curve may be referred to as 'V-dQ/dV curve'. The first peak may be a peak having the smallest V among a plurality of peaks on the differential capacity curve.

The sixteenth external variable may indicate a difference between the voltage of the first peak and a reference voltage. The reference voltage is the voltage of the first peak of the differential capacity curve obtained when the battery cell <NUM> is at Beginning Of Life (BOL), and may be preset.

The first to nth desired data sets are associated with the first to nth internal variables that are unobservable outside the battery cell <NUM>, respectively. That is, when a second index j is an integer of <NUM> to n, the jth desired data set is associated with the jth internal variable. Each internal variable indicates the internal chemical state of the battery cell <NUM>. Each desired data set includes the same number of target values as the third number. For example, the third number of values preset as the jth internal variable are included in the jth desired data set.

In the specification, Yj denotes the jth desired data set, and Yj(k) denotes the kth target value of the jth desired data set.

Y<NUM>(k) to Yn(k) are the expected values for the first to nth internal variables when the battery cell <NUM> has a particular degradation state. X<NUM>(k) to Xm(k) are the expected values for the first to mth external variables dependent on Y<NUM>(k) to Yn(k) when the battery cell <NUM> has the particular degradation state.

The degradation state of the battery cell <NUM> changes depending on the environment in which the battery cell <NUM> is used, and each degradation state may be defined by a combination of the first to nth internal variables. When a ≠ b, X<NUM>(a) to Xm(a) and Y<NUM>(a) to Yn(a) are associated with 'a' degradation state, and X<NUM>(b) to Xm(b) and Y<NUM>(b) to Yn(b) are associated with 'b' degradation state that is different from the 'a' degradation state of the battery cell <NUM>.

For example, when n = <NUM>, the first to fourteenth internal variables may be defined as follows.

The first internal variable may be the electrical conductivity of the positive electrode of the battery cell <NUM>.

The second internal variable may be the ionic diffusivity of the positive electrode active material of the battery cell <NUM>.

The third internal variable may be the rate constant of the exchange current density of the positive electrode active material of the battery cell <NUM>.

The fourth internal variable may be the ionic diffusivity of the negative active material of the battery cell <NUM>.

The fifth internal variable may be the rate constant of the exchange current density of the negative active material of the battery cell <NUM>.

The sixth internal variable may be the tortuosity of the negative electrode of the battery cell <NUM>. The tortuosity is the ratio of the distance an ion travels from one point to another to the straight-line distance between the same two points.

The seventh internal variable may be the porosity of the negative electrode of the battery cell <NUM>.

The eighth internal variable may be the ion concentration of the electrolyte of the battery cell <NUM>.

The ninth internal variable may be the scale factor by which the initial ionic conductivity of the electrolyte of the battery cell <NUM> is multiplied. The initial ionic conductivity may be a preset value indicating the ionic conductivity of the electrolyte of the battery cell <NUM> when the battery cell <NUM> is at BOL.

The tenth internal variable may be the scale factor by which the initial ionic diffusivity of the electrolyte of the battery cell <NUM> is multiplied. The initial ionic diffusivity may be a preset value indicating the ionic diffusivity of the electrolyte of the battery cell <NUM> when the battery cell <NUM> is at BOL.

The eleventh internal variable may be the cation transference number of the electrolyte of the battery cell <NUM>. The transference number indicates the fractional contribution of cations (e.g., Li+) to the electrical conductivity of the electrolyte.

The twelfth internal variable may be the Loss of Lithium Inventory (LLI) of the battery cell <NUM>. The LLI indicates the loss of lithium in the battery cell <NUM> compared to BOL.

The thirteenth internal variable may be the Loss of Active Material (LAM) of the positive electrode of the battery cell <NUM>. The LAM of the positive electrode indicates the loss of the positive electrode active material of the battery compared to the BOL.

The fourteenth internal variable may be the LAM of the negative electrode of the battery cell <NUM>. The LAM of the negative electrode indicates the loss of the negative electrode active material of the battery compared to BOL.

The first to mth input data sets and the first to nth desired data sets may be preset from the simulation results of a plurality of battery cells having the same electrochemical specifications as the battery cell <NUM>, but different degradation states.

The control unit <NUM> may determine first to nth reference values required for the modeling of the first to nth sub-multilayer perceptrons using the main multilayer perceptron <NUM> stored in the memory unit <NUM>.

Referring to <FIG>, the main multilayer perceptron <NUM> includes an input layer <NUM>, a predetermined number of intermediate layers <NUM> and an output layer <NUM>. In the main multilayer perceptron <NUM>, the number of nodes (also referred to as 'neurons') included in each layer, connections between nodes and functions of each node included in each intermediate layer may be preset. Weights for each connection may be set using predefined training data.

The input layer <NUM> includes first to mth input nodes I<NUM> to Im. The first to mth input nodes I<NUM> to Im are associated with the first to mth external variables, respectively. Each input value included in the ith input data set Xi is provided to the ith input node Ii.

The output layer <NUM> includes first to nth output nodes O<NUM> to On. The first to nth output nodes O<NUM> to On are associated with the first to nth internal variables, respectively.

When the kth input value of each of the first to mth input data sets X<NUM> to Xm is input to the first to mth input nodes I<NUM> to Im, respectively, the kth result value of each of the first to nth output data sets Z<NUM> to Zn is output from the first to nth output nodes O<NUM> to On, respectively.

The control unit <NUM> may generate first to nth output data sets Z<NUM> to Zn, each having the same number of result values as the third number by repeating the process of inputting m input values (e.g., X<NUM>(k) to Xm(k)) of the same order in the first to mth input data sets X<NUM> to Xm to the first to mth input nodes I<NUM> to Im, respectively. In the specification, Zj denotes the jth output data set, and Zj(k) denotes the kth result value of the jth output data set. In the first to nth output data sets, n result values Z<NUM>(k) to Zn(k) may be arranged in the same order.

The control unit <NUM> may determine first to nth error factors based on the first to nth output data sets and the first to nth desired data sets. That is, the control unit <NUM> may determine the jth error factor by comparing the jth output data set with the jth desired data set. Specifically, the control unit <NUM> may determine the jth error factor using the following Equation <NUM>.

In Equation <NUM>, u is the third number, and Ferror_j is the jth error factor. That is, the jth error factor may be determined to be equal to an error ratio of the jth output data set to the jth desired data set.

The control unit <NUM> may determine the first to nth reference values by comparing each of the first to nth error factors with a threshold error factor. The threshold error factor may be a preset value, for example, <NUM>%. Specifically, when the jth error factor is smaller than the threshold error factor, the control unit <NUM> may determine the jth reference value to be equal to a first predetermined value. On the contrary, when the jth error factor is equal to or larger than the threshold error factor, the control unit <NUM> may determine the jth reference value to be equal to a second predetermined value. The second predetermined value (e.g., <NUM>) may be smaller than the first predetermined value (e.g., <NUM>). After determining the first to nth reference values, the process using the main multilayer perceptron <NUM> may be completed. Alternatively, each of the first to nth reference values may be preset to the first predetermined value or the second predetermined value. In this case, the process using the main multilayer perceptron <NUM> may be omitted.

The control unit <NUM> performs the process for determining the first to nth sub-multilayer perceptrons.

The control unit <NUM> determines a multiple correlation coefficient between each of the first to nth desired data sets and each of the first to mth input data sets. The multiple correlation coefficient between the ith input data set and the jth desired data set may be determined from the following Equation <NUM>.

In Equation <NUM>, N is the third number, and ri,j is the multiple correlation coefficient between the ith input data set and the jth desired data set. As m multiple correlation coefficients are determined for respective desired data set, a total of m×n multiple correlation coefficients may be determined.

The absolute value of the multiple correlation coefficient ri,j that is larger than the jth reference value indicates a high correlation between the ith external variable and the jth internal variable. When the absolute value of the multiple correlation coefficient ri,j is larger than the jth reference value, the control unit <NUM> may determine to use the ith input data set for the modeling of the jth sub-multilayer perceptron. When the absolute value of the multiple correlation coefficient ri,j is larger than the jth reference value, the control unit <NUM> may set the ith external variable as a valid external variable for the jth internal variable.

On the contrary, the absolute value of the multiple correlation coefficient ri,j that is equal to or less than the jth reference value indicates a low correlation between the ith external variable and the jth internal variable. When the absolute value of the multiple correlation coefficient ri,j is equal to or less than the jth reference value, the control unit <NUM> may determine not to use the ith input data set for the modeling of the jth sub-multilayer perceptron. That is, the control unit <NUM> may exclude the ith external variable from a valid external variable for the jth internal variable.

The control unit <NUM> may generate the first to nth sub-multilayer perceptrons associated with the first to nth internal variables, respectively. That is, the jth internal variable and the jth sub-multilayer perceptron are associated with each other. The jth sub-multilayer perceptron is used to estimate a value of the jth internal variable.

Referring to <FIG>, the jth sub-multilayer perceptron <NUM>j includes an input layer <NUM>j, a predetermined number of intermediate layers <NUM>j and an output layer <NUM>j. The function of each node included in each intermediate layer <NUM>j may be preset. The output layer <NUM>j has a single output node associated with the jth internal variable.

The control unit <NUM> may generate the same number of input nodes of the input layer <NUM>j of the jth sub-multilayer perceptron as the number of valid external variables sets for the jth internal variable. <FIG> exemplarily shows that each of the first, third, and fifth external variables is set as the valid external variable for the jth internal variable.

The input layer <NUM>j of the jth sub-multilayer perceptron has three input nodes Ij1 to Ij3. The first, third and fifth input data sets respectively associated with the three external variables are provided as training data to each of the three input nodes Ij1 to Ij3, and learning of the jth sub-multilayer perceptron 300j is performed through comparison between the result values of the data set Wj obtained from the output layer <NUM>j of the jth sub-multilayer perceptron and the target values of the jth desired data set.

After the learning of the jth sub-multilayer perceptron <NUM>j is completed, the control unit <NUM> may measure the voltage, the current and the temperature of the battery cell <NUM> using the sensing unit <NUM>. The control unit <NUM> may determine values of three external variables based on the sensing data from the sensing unit <NUM>. Subsequently, the control unit <NUM> may obtain a result value in the output layer <NUM>j of the jth sub-multilayer perceptron <NUM>j by inputting the values of the three external variables to the three input nodes Ij1 to Ij3 of the jth sub-multilayer perceptron <NUM>j, respectively. The corresponding result value is an estimated value of the jth internal variable associated with the degradation state corresponding to the values of the three external variables. When the estimated value of the jth internal variable is outside of a predetermined jth safety range, the control unit <NUM> may control the switch <NUM> into the open operating state to protect the battery cell <NUM>.

<FIG> is a flowchart exemplarily showing a first method that may be performed by the battery management apparatus <NUM> of <FIG>. The first method according to <FIG> is a data filtering method for extracting at least one of the first to pth observational data sets as an input data set.

Referring to <FIG>, in step S410, the control unit <NUM> stores first to pth observational data sets and first to nth desired data sets in the memory unit <NUM>.

In step S420, the control unit <NUM> sets each of the first index q and the second index m to <NUM>.

In step S430, the control unit <NUM> determines a multiple correlation coefficient between the qth observational data set and the q+<NUM>th observational data set. For example, where q=<NUM>, the multiple correlation coefficient between the first observational data set and the second observational data set is determined. The multiple correlation coefficient between the qth observational data set and the q+<NUM>th observational data set may be determined from the following Equation <NUM>. In the specification, Pq denotes the qth observational data set.

In Equation <NUM>, N is the third number, Pq(k) is the kth input value of the qth observational data set, Pq+<NUM>(k) is the kth input value of the q+<NUM>th observational data set, and Rq,q+<NUM> is the multiple correlation coefficient between the qth observational data set and the q+<NUM>th observational data set.

In step S440, the control unit <NUM> determines whether the absolute value of the multiple correlation coefficient Rq,q+<NUM> is less than a predetermined filtering value (for example, <NUM>). A value of the step S440 being "Yes" represents that the correlation between the qth observational data set and the q+<NUM>th observational data set is not sufficiently high. Accordingly, it is very necessary to set the qth observational data set as the input data set. When the value of the step S440 is "Yes", step S450 is performed. On the contrary, the value of the step S440 being "No" represents that the correlation between the qth observational data set and the q+<NUM>th observational data set is sufficiently high. Accordingly, the need for set the qth observational data set as the input data set is low. When the value of the step S440 is "No", step S470 is performed.

In step S450, the control unit <NUM> sets the qth observational data set as the mth input data set. In an example, where q=<NUM>, m=<NUM>, the first input data set is equal to the first observational data set. In another example, where q=<NUM>, m=<NUM>, the second input data set is equal to the third observational data set.

In step S460, the control unit <NUM> increases the second index m by <NUM>.

In step S470, the control unit <NUM> determines whether the first index q is equal to p-<NUM>. p is the number of observational data sets. When a value of the step S470 is "No", step S480 is performed. When the value of the step S470 is "Yes", step S490 is performed.

In step S490, the control unit <NUM> sets the q+<NUM>th observational data set as the mth input data set. By the step S490, among the first to pth observational data sets, at least the pth observational data set is set as the input data set.

By the steps S430 ~ S450 of <FIG>, when the two observational data sets have a strong correlation, only one of them is extracted as the input data set. Compared to the case in which all observational data sets are used as input data sets without exception, it is possible to reduce the amount of computation required for the modeling of the first to nth sub-multilayer perceptrons and increase the computation rate.

After the first to mth input data sets are determined by the method of <FIG>, the control unit <NUM> may perform the method of <FIG>. Alternatively, the control unit <NUM> may use the first to pth observational data sets as the first to mth input data sets without performing the method according to <FIG>, and in this case, p=m.

<FIG> is a flowchart exemplarily showing a second method that may be performed by the battery management apparatus <NUM> of <FIG>.

Referring to <FIG>, in step S510, the control unit <NUM> acquires first to nth output data sets from first to mth input data sets using the main multilayer perceptron <NUM>.

In step S520, the control unit <NUM> sets the third index j to <NUM>.

In step S530, the control unit <NUM> determines the jth error factor based on the jth output data set and the jth desired data set.

In step S540, the control unit <NUM> determines whether the jth error factor is less than the threshold error factor. When a value of the step S540 is "Yes", step S550 is performed. When the value of the step S540 is "No", step S560 is performed.

In step S550, the control unit <NUM> sets the jth reference value to be equal to the first predetermined value.

In step S560, the control unit <NUM> sets the jth reference value to be equal to the second predetermined value.

In step S570, the control unit <NUM> determines whether the third index j is equal to n. n is the number of desired data sets. When a value of the step S570 is "No", step S580 is performed.

In step S580, the control unit <NUM> increases the second index j by <NUM>. After the step S580, the process returns to the step S530.

After the first to nth reference values are determined by the method of <FIG>, the control unit <NUM> may perform the method of <FIG>. Alternatively, when the first to nth reference values are preset and stored in the memory unit <NUM> as described above, the control unit <NUM> may perform the method of <FIG> without performing the method of <FIG>.

<FIG> is a flowchart exemplarily showing a third method that may be performed by the battery management apparatus <NUM> of <FIG>. The third method according to <FIG> may be a data filtering method for extracting an input data set used for machine learning of each sub-multilayer perceptron from the first to mth input data sets.

Referring to <FIG>, in step S610, the control unit <NUM> sets each of the third index j and a fourth index i to <NUM>.

In step S620, the control unit <NUM> determines a multiple correlation coefficient between the ith input data set and the jth desired data set.

In step S630, the control unit <NUM> determines whether the absolute value of the multiple correlation coefficient is larger than the jth reference value. When a value of the step S630 is "Yes", step S640 is performed. When the value of the step S630 is "No", step S650 is performed.

In step S640, the control unit <NUM> sets the ith external variable as a valid external variable for the jth internal variable.

In step S650, the control unit <NUM> determines whether the fourth index i is equal to m. m is the number of input data sets. When a value of the step S650 is "No", step S660 is performed. When the value of the step S650 is "Yes", step S670 is performed.

In step S660, the control unit <NUM> increases the fourth index i by <NUM>. After the step S660, the process returns to the step S620.

In step S670, the control unit <NUM> determines whether the third index j is equal to n. When a value of the step S670 is "No", step S680 is performed.

In step S680, the control unit <NUM> increases the third index j by <NUM>, and sets the fourth index i to <NUM>. After the step S680, the process returns to the step S620.

The value of the step S650 being "Yes" indicates that the setting of the valid external variable for the jth internal variable is completed. Each time the value of the step S650 is "Yes", the control unit <NUM> may generate the jth sub-multilayer perceptron used to estimate the value of the jth internal variable.

The embodiments of the present disclosure described hereinabove are not implemented only through the apparatus and method, and may be implemented through programs that perform functions corresponding to the configurations of the embodiments of the present disclosure or recording media having the programs recorded thereon, and such implementation may be easily achieved by those skilled in the art from the disclosure of the embodiments previously described.

While the present disclosure has been hereinabove described with regard to a limited number of embodiments and drawings, the present disclosure is not limited thereto and it is obvious to those skilled in the art that various modifications and changes may be made thereto within the technical aspects of the present disclosure.

Claim 1:
A battery management apparatus (<NUM>), comprising:
a memory unit (<NUM>) configured to store first to pth observational data sets and first to nth desired data sets, wherein each of p and n is an integer of <NUM> or greater; and
a control unit (<NUM>) operably coupled to the memory unit (<NUM>),
wherein the first to pth observational data sets are respectively associated with p external variables that are observable outside a battery cell,
each of the first to pth observational data sets includes a predetermined number of input values,
the first to nth desired data sets are associated with first to nth internal variables, respectively, wherein the first to nth internal variables are dependent on an internal chemical state of the battery cell and unobservable outside the battery cell,
each of the first to nth desired data sets includes the same number of target values as the predetermined number, and
the control unit (<NUM>) is configured to:
set m observational data sets extracted by data filtering from the first to pth observational data sets as first to mth input data sets, wherein m is an integer with <NUM> ≤ m ≤ p, by determining a multiple correlation coefficient between the qth observational data set and the q+<NUM>th observational data set, when q is an integer of <NUM> to p-<NUM>, and
setting the qth observational data set as one of the first to mth input data sets when an absolute value of the multiple correlation coefficient is less than a predetermined filtering value, and
set at least one of the first to mth external variables as a valid external variable for each of the first to nth internal variables based on the first to mth input data sets and the first to nth desired data sets by analyzing a correlation between the internal variables and the external variables, and
wherein the first to mth external variables are m external variables associated with the m observational data sets, among the p external variables.