KR20220034419A - battery management system and method for estimating battery resistance - Google Patents

Abstract

A memory of a battery managing system stores a correlation between initial resistance and the temperature of the battery when a battery is under an initial condition. A processor of the battery managing system estimates internal resistance of the battery based on a measurement voltage of the battery, a measurement current of the battery, an open circuit voltage of the battery, estimates the temperature of the battery based on the measurement temperature of the battery, extracts the initial resistance corresponding to the estimated temperature from the memory, and estimates a resistance condition of the battery based on the extracted initial resistance and the internal resistance.

Description

Battery management system and resistance state estimation method {battery management system and method for estimating battery resistance}

The present invention relates to a battery management system and a method for estimating a resistance state.

An electric vehicle or a hybrid vehicle is a vehicle that obtains power by driving a motor mainly using a battery as a power source, and research is being actively conducted in that it is an alternative to the pollution and energy problems of an internal combustion vehicle. In addition, rechargeable batteries are used in various external devices other than electric vehicles.

A battery includes a positive electrode and a negative electrode, a separator interposed between the electrodes, and an electrolyte that electrochemically reacts with an active material coated on the positive and negative electrodes, and the capacity decreases as the number of charge and discharge increases. The cause of the decrease in capacity can be found in the deterioration of the active material coated on the electrode, the side reaction of the electrolyte, and the decrease in the pores of the separator. As the capacity of the battery decreases, the resistance increases, increasing the electrical energy dissipated as heat. Therefore, when the capacity of the battery decreases below the threshold, the performance of the battery remarkably deteriorates and the amount of heat generated increases, so inspection or replacement is required.

In the field of battery technology, the degree of capacity reduction of a battery may be quantitatively expressed by a factor called a state of health (SOH). SOH can be calculated in several ways, one of which can be calculated by quantifying the degree of increase in the resistance of the battery based on the current time compared to the resistance in the beginning of life (BOL) state. . For example, if the resistance of the battery is increased by 20% compared to the resistance in the BOL state, it can be estimated that the SOH is 80%. Battery life can be extended by controlling the maximum current based on the SOH. To realize this, it is necessary to accurately detect the internal resistance of the battery and estimate the state of the internal resistance.

Based on the previously measured resistance data, the resistance state, ie, the degree of deterioration of the resistance, may be estimated to the extent that the internal resistance estimated at a specific temperature and a specific SOC condition is increased compared to the resistance when the battery is in the initial state. In addition, the estimated resistance state is applied to all conditions under the assumption that the internal resistance has deteriorated to the same level even at a temperature or SOC different from a specific temperature or a specific SOC, and is used for output estimation. However, the resistance of the battery may vary depending on the temperature, and if the estimated resistance at a certain temperature is applied to other temperatures, a result different from the actual resistance state may occur.

An object of the present invention is to provide a battery management system and a resistance state estimation method capable of accurately estimating internal resistance and resistance state of a battery.

According to one embodiment of the present invention, there is provided a battery management system including a memory and a processor. The memory stores a correlation between an initial resistance when the battery is in an initial state and a temperature of the battery. The processor estimates the internal resistance of the battery based on the measured voltage of the battery, the measured current of the battery and the open circuit voltage of the battery, estimates the temperature of the battery based on the measured temperature of the battery, and The initial resistance corresponding to the estimated temperature is extracted from the memory, and the resistance state of the battery is estimated based on the extracted initial resistance and the internal resistance.

The processor may input the measured voltage, the measured current, and the open circuit voltage to a first adaptive filter to estimate the internal resistance, and input the measured temperature to a second adaptive filter to estimate the temperature.

The first adaptive filter and the second adaptive filter may each include a recursive least squares (RLS) filter.

The measured voltage or the measured current measured at a previous time is reflected in the internal resistance estimated at the current time by the processor, and the temperature estimated by the processor at the current time includes the measured temperature measured at the previous time may be reflected.

The processor may estimate a state of charge of the battery based on the measured voltage or the measured current, and estimate the open circuit voltage based on the state of charge.

The measured voltage is determined based on voltages measured by a plurality of battery cells included in the battery, and the measured current may be a current measured by a current sensor.

The processor may estimate the resistance state to the extent that the internal resistance is increased compared to the initial resistance.

According to another embodiment of the present invention, a method for estimating a resistance state of a battery is provided. The resistance state estimation method includes estimating the internal resistance of the battery based on the measured voltage of the battery, the measured current of the battery and the open circuit voltage of the battery, and determining the temperature of the battery based on the measured temperature of the battery estimating, extracting an initial resistance when the battery is in an initial state corresponding to the estimated temperature, and estimating a resistance state of the battery based on the initial resistance and the internal resistance.

According to another embodiment of the present invention, a program executed by a processor of a battery device and stored in a recording medium is provided. The program may include, by the processor, estimating, by the processor, an internal resistance of the battery based on a measured voltage of the battery, a measured current of the battery, and an open circuit voltage of the battery, a temperature of the battery based on the measured temperature of the battery estimating, extracting an initial resistance when the battery is in an initial state corresponding to the estimated temperature, and estimating a resistance state of the battery based on the initial resistance and the internal resistance. do.

According to an embodiment of the present invention, it is possible to accurately estimate the resistance state by extracting the BOL resistance corresponding to the temperature of the battery. According to another embodiment of the present invention, by reflecting the delayed information similar to the estimation of the internal resistance of the battery to the temperature used for extracting the BOL resistance, it is possible to remove the error due to the temperature difference at the measurement time.

1 is a diagram illustrating a battery device according to an embodiment of the present invention.
2 is a view for explaining estimation of a resistance state in a battery management system according to an embodiment of the present invention.
3 is a flowchart illustrating a method for estimating a resistance state in a battery management system according to an embodiment of the present invention.
4 is a diagram illustrating an equivalent circuit of a battery according to an embodiment of the present invention.
5 is a diagram illustrating an example of a temperature-resistance profile in a battery according to an embodiment of the present invention.
6 is a flowchart illustrating a method for estimating internal resistance in a battery management system according to an embodiment of the present invention.
7 is a view for explaining a resistance state estimation in a battery management system according to another embodiment of the present invention.
8 is a flowchart illustrating a method for estimating a resistance state in a battery management system according to an embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings, the embodiments of the present invention will be described in detail so that those of ordinary skill in the art to which the present invention pertains can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

When a component is referred to as being “connected” to another component, it may be directly connected to the other component, but it should be understood that other components may exist in between. On the other hand, when it is mentioned that a certain element is "directly connected" to another element, it should be understood that the other element does not exist in the middle.

In the description below, expressions described in the singular may be construed in the singular or plural unless an explicit expression such as “one” or “single” is used.

In the flowchart described with reference to the drawings, the order of operations may be changed, several operations may be merged, some operations may be divided, and specific operations may not be performed.

1 is a diagram illustrating a battery device according to an embodiment of the present invention.

Referring to FIG. 1 , the battery device 100 has a structure that can be electrically connected to an external device. When the external device is a load, the battery device 100 operates as a power supply that supplies power to the load and is discharged. When the external device is a charger, the battery device 100 is charged by receiving external power through the charger. The external device operating as a load may be, for example, an electronic device, a transportation means, or an energy storage system (ESS), and the transportation means may be, for example, an electric vehicle, a hybrid vehicle, or smart mobility. there is.

The battery device 100 includes a battery 110 , a voltage measuring circuit 120 , a temperature sensor 130 , a current sensor 140 , a processor 150 , and a memory 160 .

The battery 110 is a rechargeable secondary battery. The battery 110 includes a single battery cell, an assembly of a plurality of battery cells, or a battery module in which a plurality of assemblies are connected in series or parallel, a battery pack in which a plurality of battery modules are connected in series or parallel, or a plurality of battery packs in series or parallel It may be a connected system.

The voltage measuring circuit 120 measures the voltage of the battery 110 . In some embodiments, the voltage measurement circuit 120 may measure the voltage of each battery cell.

The temperature sensor 130 measures the temperature of the battery 110 . In some embodiments, the temperature sensor 130 may measure the temperature of a predetermined location of the battery 110 . In some embodiments, a plurality of temperature sensors 130 may be provided to measure the temperature of a plurality of locations in the battery 110 .

The current sensor 140 is connected to a positive terminal or a negative terminal of the battery 110 , and measures the current of the battery 110 , that is, a charging current or a discharging current.

The processor 150 includes the voltage of the battery 110 measured by the voltage measuring circuit 120 , the temperature of the battery 110 measured by the temperature sensor 130 , and the current of the battery 110 measured by the current sensor 140 . Based on the , the resistance state of the battery 110 is estimated. In some embodiments, the processor 150 may refer to a table stored in the memory 160 for estimating the resistance state of the battery 110 .

The memory 160 stores a table used for estimating the resistance state of the processor 150 . In some embodiments, the memory 160 may store instructions used in the operation of the processor 150 . In some embodiments, memory 160 may be embedded in processor 150 or coupled to processor 150 via a bus. In some embodiments, the memory 160 storing the table may be a non-volatile memory.

In some embodiments, processor 150 and memory 160 may form a battery management system. In some embodiments, the battery management system may further include at least one of a voltage measurement circuit 120 , a temperature sensor 130 , or a current sensor 140 .

FIG. 2 is a diagram illustrating resistance state estimation in a battery management system according to an embodiment of the present invention, and FIG. 3 is a flowchart illustrating a resistance state estimation method in a battery management system according to an embodiment of the present invention. 4 is a diagram illustrating an equivalent circuit of a battery according to an embodiment of the present invention, and FIG. 5 is a diagram illustrating an example of a temperature-resistance profile in a battery according to an embodiment of the present invention. 6 is a flowchart illustrating a method for estimating internal resistance in a battery management system according to an embodiment of the present invention.

2 and 3 , the processor (eg, 150 in FIG. 1 ) of the battery management system uses the adaptive filter 210 to estimate the internal resistance of the battery (eg, 110 in FIG. 1 ). It can be (S310, S320). The processor 150 inputs the current (I) of the battery 110, the voltage (V) of the battery 110, and the open circuit voltage (OCV) of the battery 110 to the adaptive filter 210 ( S310), the internal resistance may be estimated through the adaptive filter 210 (S320). In some embodiments, the current of the battery 110 may be the charging or discharging current of the battery 110 measured by a current sensor (eg, 140 in FIG. 1 ). In some embodiments, the voltage of the battery 110 may be an average cell voltage, and the average cell voltage may be an average value of voltages of a plurality of battery cells. In some embodiments, the voltage of the battery 110 may be the sum of the voltages of a plurality of battery cells. The open circuit voltage of the battery 110 may be a value converted from the average state of charge of the battery 110 .

Referring to FIG. 4 , the equivalent circuit model of the battery includes an open circuit voltage source 410 , a series resistor 420 , and an RC parallel circuit.

The open circuit voltage source 410 simulates the open circuit voltage, which is the voltage between the positive and negative electrodes of an electrochemically stabilized battery. The open circuit voltage may be determined by the state of charge (SOC) of the battery 110 and may have a non-linear functional relationship with the state of charge (OCV=f(SOC)). In some embodiments, the memory of the battery management system (eg, 160 in FIG. 1 ) stores the SOC-OCV profile defining the correlation between the open circuit voltage of the battery 110 and the state of charge in the form of a lookup table or the like. may be doing In another embodiment, the memory 160 may store the SOC-OCV profile defining a correlation between the open circuit voltage of the battery 110 and the state of charge for each temperature. Accordingly, the processor 150 may determine the open circuit voltage associated with the state of charge by referring to the SOC-OCV profile stored in the memory 160 .

In some embodiments, the processor 150 may determine the state of charge of the battery 110 based on a voltage of the battery 110 , a current of the battery 110 , or a temperature of the battery 110 . The processor 150 may determine the state of charge by using any one of a variety of known methods, and the present invention is not limited to the method of determining the state of charge.

The series resistance 420 simulates the internal resistance of the battery 110 representing the voltage drop inside the battery 110 due to the current flowing in the battery 110, and the instantaneous moment of the battery terminal voltage due to the current flowing in the battery 110. indicates drastic change. The RC parallel circuit includes a resistor 431 and a capacitor 432 connected in parallel as simulating a transient change of a polarization voltage reflected in the battery terminal voltage, that is, over-potential.

In this equivalent circuit model, the battery terminal voltage (V) may be given as in Equation (1).

In Equation 1, R ₀ is the resistance value of the internal resistor 420, R ₁ is the resistance value of the resistor 431 of the RC parallel circuit, C ₁ is the capacitance of the capacitor 432 of the RC parallel circuit, I is the current of the battery 110, and OCV is the open circuit voltage.

Referring back to FIGS. 2 and 3 , the processor 150 extracts the BOL resistance based on the temperature T of the battery 110 through the BOL resistance extraction module 220 ( S330 ). In some embodiments, the temperature of the battery 110 may be an average temperature that is an average value of temperatures measured by a plurality of temperature sensors. BOL resistance is the internal resistance of the battery when it is in the beginning of life (BOL) state. As shown in FIG. 5 , the memory 160 may store a temperature-resistance profile defining a correlation between temperature and BOL resistance in the form of, for example, a lookup table. In some embodiments, the BOL resistance for each temperature may be a value determined according to the specifications of the battery. The BOL resistance extraction module 220 may extract a BOL resistance corresponding to the input temperature of the battery 110 from the temperature-resistance profile.

The processor 150 calculates the resistance state based on the internal resistance of the battery 110 estimated by the adaptive filter 210 through the resistance state calculation module 230 and the BOL resistance extracted by the BOL resistance extraction module 220 ( S340). In some embodiments, the processor 150 may estimate the resistance state SOHR as a value obtained by subtracting the increase rate of the internal resistance relative to the BOL resistance from 100% as shown in Equation (2). This resistance state may be referred to as a resistance degradation state.

In Equation 2, R ₀ is the estimated internal resistance of the battery, and R _BOL is the extracted BOL resistance.

As described above, according to an embodiment of the present invention, internal resistance may be estimated by inputting information measured from the battery into the adaptive filter 210 . In addition, when comparing the BOL resistance to determine how much the estimated internal resistance has deteriorated, the battery temperature is considered and compared, so that the resistance state can be accurately estimated.

Next, an example of a method of estimating the internal resistance through the adaptive filter 210 will be described with reference to FIG. 6 . In some embodiments, a recursive least squares (RLS) filter may be used as the adaptive filter 210 , and the adaptive filter 210 will be described below as an RLS filter.

If Equation 1 is rearranged, it can be expressed as Equation 3.

In some embodiments, the memory 160 may store R ₁ and C ₁ in the form of a lookup table. In some embodiments, the memory 160 may store R ₁ and C ₁ corresponding to measured information (eg, voltage or current) of the battery in the form of a lookup table.

)는 수학식 5와 6과 같이 표현될 수 있으며, 수학식 4에서 k 시점에서의 관찰 데이터 행렬(H _k )은 수학식 7과 같이 표현할 수 있다. 따라서, 프로세서(150)는 적응 필터(210)에 k 시점에서 측정된 시스템 출력(y _k )과 관찰 데이터 행렬(H _k )을 입력한다(S610).When Equation 3 is expressed as a determinant such as Equation 4, the system output ( y _k ) at time k indicating the current time point and the estimation parameter (

) can be expressed as in Equations 5 and 6, and in Equation 4, the observation data matrix H _k at k time can be expressed as in Equation 7. Accordingly, the processor 150 inputs the system output y _k measured at time k and the observation data matrix H _k to the adaptive filter 210 ( S610 ).

In Equations 5 and 7, V _k is the voltage of the battery measured at time k (eg, average cell voltage), OCV _k is the open circuit voltage estimated based on the SOC at time k, and I _k is k The current of the battery measured at the time (eg, the current measured by the current sensor).

The adaptive filter 210 calculates a gain K _k ( S620 ). For example, the adaptive filter 210 may calculate the gain ( K _k ) as in Equation (8).

In Equation 8, P _k-1 represents covariance, and R _k represents a forgetting factor. In some embodiments, the covariance P _k-1 may be an updated value as in Equation 11 at the time point (k-1). In some embodiments, the forgetting factor R _k may be calculated as the covariance of the measured noise.

)로 k 시점에서의 시스템 출력을 추정한다(S630). 추정 출력은 예를 들면

와 같이 주어질 수 있다. 또한 적응 필터(210)는 k에서의 실제 시스템 출력(y _k )과 추정 출력 사이의 오차를 계산한다(S630). 오차는 예를 들면 (

)와 같이 주어질 수 있다. 이에 따라, 적응 필터(210)는 오차에 이득을 반영하여서 k에서의 추정 파라미터(

)를 갱신한다(S640). 예를 들면, 적응 필터(210)는 수학식 9와 같이 추정 파라미터(

)를 갱신할 수 있다.The adaptive filter 210 calculates the estimated parameter (

) to estimate the system output at time k (S630). The estimated output is for example

can be given as Also, the adaptive filter 210 calculates an error between the actual system output y _k at k and the estimated output ( S630 ). The error is for example (

) can be given as Accordingly, the adaptive filter 210 reflects the gain in the error to determine the estimated parameter (

) is updated (S640). For example, the adaptive filter 210 uses the estimation parameter (

) can be updated.

)에 기초해서 내부 저항의 저항값(R₀)를 계산한다(S650).Since R ₁ and C ₁ in the estimation parameter are given values, the adaptive filter 210 calculates R ₁ , C ₁ and the updated estimation parameter (

) based on the internal resistance resistance (R ₀ ) is calculated (S650).

)과 공분산의 초기값(P ₀ )이 미리 정해질 수 있다. 예를 들면, 추정 파라미터의 초기값(

)과 공분산의 초기값(P ₀ )으로 수학식 10과 같이 정해질 수 있다.On the other hand, for the calculation of Equations 8 and 9, the initial value of the estimation parameter (

) and an initial value ( P ₀ ) of the covariance may be predetermined. For example, the initial value of the estimation parameter (

) and the initial value ( P ₀ ) of the covariance may be determined as in Equation 10.

)에 기초해서 갱신할 수 있다Next, the adaptive filter 210 updates the factor to be used at the time (k+1) (S660). In some embodiments, it is possible to update the covariance ( P _k ) to be used at time (k+1). For example, the adaptive filter 210 may update the covariance P _k as in Equation (11). In some embodiments, the adaptive filter 210 may further update the forgetting factor R _k . For example, the adaptive filter 210 calculates the forgetting factor ( R _k ) previously calculated error (

) can be updated based on

Through this process, the RLS filter estimates the estimation parameter, that is, the internal resistance (R ₀ ) at each time point (k), and updates the factors used for estimation (eg, covariance, forgetting factor) at the next time point (k+ It can be provided for the estimation in 1). Accordingly, when the estimation of the internal resistance is not completed (S670), the adaptive filter 210 may continue to estimate the next time (S680).

The internal resistance estimation method described with reference to FIG. 6 is an example of using the RLS filter as the adaptive filter 210, and the present invention is not limited thereto.

) 및 (k-1) 시점에서 갱신된 공분산(P _k-1 )을 사용하며, 공분산(P _k-1 )도 (k-1) 시점에서 측정된 정보에 망각 팩터가 반영되어서 결정된다. 따라서, 현재 시점(k)에서의 내부 저항의 추정에 이전 시점(k-1)에서 측정된 정보, 즉 지연된 정보가 사용되는 반면, 프로세서(150)가 BOL 저항을 추출할 때는 배터리의 현재 온도를 사용하고 이전 시점에서의 온도를 사용하지 않는다. 이에 따라, 이전 시점과 현재 시점에서의 온도 차이가 큰 경우, 저항 상태 추정이 정확하지 않을 수 있다.As described above, when estimating the internal resistance at time k, the gain ( K _k ) is the estimated parameter (

) and (k-1), the updated covariance ( P _k-1 ) is used, and the covariance ( P _k-1 ) is also determined by reflecting the forgetting factor in the information measured at time (k-1). Accordingly, information measured at a previous time point (k-1), that is, delayed information, is used for estimating the internal resistance at the current time point (k), whereas when the processor 150 extracts the BOL resistance, the current temperature of the battery is used. Use and do not use the temperature at the previous time point. Accordingly, when the temperature difference between the previous time point and the current time point is large, the estimation of the resistance state may not be accurate.

7 is a diagram for explaining resistance state estimation in a battery management system according to another embodiment of the present invention, and FIG. 8 is a flowchart illustrating a resistance state estimation method in the battery management system according to an embodiment of the present invention.

7 and 8 , the processor (eg, 150 in FIG. 1 ) of the battery management system uses the adaptive filter 510 to estimate the internal resistance of the battery (eg, 110 in FIG. 1 ). It can be (S810, S820). As described with reference to FIGS. 2 and 3 , the processor 150 adapts the current I of the battery 110 , the voltage V of the battery 110 and the open circuit voltage OCV of the battery 110 . It may be input to the filter 510 (S810), and internal resistance may be estimated through the adaptive filter 510 (S820).

)를 추정할 수 있다. 어떤 실시예에서, 측정된 배터리(110)의 온도를 복수의 온도 센서에서 측정된 온도의 평균값일 수 있다.Also, the processor 150 may estimate the temperature of the battery to which the delayed information is reflected by using the adaptive filter 540 ( S830 and S840 ). The processor 150 inputs the measured temperature (T) of the battery 110 to the adaptive filter 540 (S830), and through the adaptive filter 540, the temperature (

) can be estimated. In some embodiments, the measured temperature of the battery 110 may be an average value of temperatures measured by a plurality of temperature sensors.

)는 추정된 배터리(110)의 온도이고, k 시점에서의 관찰 데이터 행렬(H _k )은 항등 행렬(identity matrix)이다. 어떤 실시예에서, 관찰 데이터 행렬(H _k )은 1×1 항등 행렬일 수 있다.When the adaptive filter 540 is expressed by a determinant such as Equation 4, the system output y _k at time k indicating the current time is the temperature of the battery 110 measured at time k, and the estimated parameter (

) is the estimated temperature of the battery 110 , and the observation data matrix H _k at time k is an identity matrix. In some embodiments, the observation data matrix H _k may be a 1×1 identity matrix.

), 즉 온도(

)를 추정할 수 있다. 따라서, 적응 필터(540)에서 추정된 온도(

)에는 이전 시점(k-1)에서 측정된 정보가 반영될 수 있다.As described above with reference to Equations 8 to 11, the adaptive filter 540 is an estimation parameter (

), that is, the temperature (

) can be estimated. Therefore, the temperature estimated in the adaptive filter 540 (

), information measured at the previous time point (k-1) may be reflected.

Next, the processor 150 extracts the BOL resistance based on the temperature of the battery 110 estimated by the adaptive filter 540 through the BOL resistance extraction module 520 ( S850 ). The BOL resistance extraction module 520 may extract a BOL resistance corresponding to the temperature of the battery 110 estimated by the adaptive filter 540 from the temperature-resistance profile.

The processor 150 calculates the resistance state based on the internal resistance of the battery 110 estimated by the adaptive filter 210 through the resistance state calculation module 530 and the BOL resistance extracted by the BOL resistance extraction module 520 ( S860). In some embodiments, the processor 150 may estimate the resistance state as a value obtained by subtracting an increase rate of the internal resistance relative to the BOL resistance from 100% as shown in Equation (2).

As described above, according to another embodiment of the present invention, internal resistance may be estimated by inputting information measured from the battery into the adaptive filter 510 . In addition, by inputting the temperature measured by the battery in the adaptive filter 540, the temperature to which the delayed information is reflected may be estimated. In this way, the resistance state can be accurately estimated based on the internal resistance to which the delayed information is reflected and the temperature to which the delayed information is reflected.

In some embodiments, the processor (eg, 150 of FIG. 1 ) may perform an operation on a program for executing the above-described resistance state estimation method or internal resistance estimation method. A program for executing the resistance state estimation method or the internal resistance estimation method may be loaded into the memory. This memory may be the same memory as the memory for storing the table (eg, 160 in FIG. 1 ) or a separate memory. The program may include instructions for causing the processor 150 to perform a resistance state estimation method or an internal resistance estimation method when loaded into a memory. That is, the processor may perform an operation for a resistance state estimation method or an internal resistance estimation method by executing a program instruction.

Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto. is within the scope of the right.

Claims (12)

A battery management system for a battery, comprising:
a memory for storing a correlation between an initial resistance when the battery is in an initial state and a temperature of the battery; and
estimating the internal resistance of the battery based on the measured voltage of the battery, the measured current of the battery, and the open circuit voltage of the battery, estimating the temperature of the battery based on the measured temperature of the battery, and the estimated temperature The processor extracts the initial resistance corresponding to
A battery management system comprising a. In claim 1,
the processor inputs the measured voltage, the measured current and the open circuit voltage to a first adaptive filter to estimate the internal resistance, and inputs the measured temperature to a second adaptive filter to estimate the temperature. . In claim 2,
wherein the first adaptive filter and the second adaptive filter each include a recursive least squares (RLS) filter. In claim 1,
The measured voltage or the measured current measured at a previous time is reflected in the internal resistance estimated by the processor at the current time,
The temperature estimated at the current time by the processor reflects the measured temperature measured at the previous time.
battery management system. In claim 1,
and the processor estimates a state of charge of the battery based on the measured voltage or the measured current, and estimates the open circuit voltage based on the state of charge. In claim 1,
The measured voltage is determined based on voltages measured in a plurality of battery cells included in the battery,
The measured current is the current measured by the current sensor.
battery management system. In claim 1,
The processor estimates the resistance state to the extent that the internal resistance is increased compared to the initial resistance. A method for estimating the resistance state of a battery, comprising:
estimating the internal resistance of the battery based on the measured voltage of the battery, the measured current of the battery and the open circuit voltage of the battery;
estimating the temperature of the battery based on the measured temperature of the battery;
extracting an initial resistance when the battery is in an initial state corresponding to the estimated temperature, and
estimating a resistance state of the battery based on the initial resistance and the internal resistance;
A resistance state estimation method comprising In claim 8,
estimating the internal resistance comprises inputting the measured voltage, the measured current and the open circuit voltage to a first adaptive filter to estimate the internal resistance,
The step of estimating the temperature includes the step of estimating the temperature by inputting the measured temperature into a second adaptive filter
A method of estimating the state of resistance. In claim 8,
The measured voltage or the measured current measured at a previous time is reflected in the internal resistance estimated at the present time,
The temperature estimated at the current time point reflects the measured temperature measured at the previous time point.
A method of estimating the state of resistance. In claim 8,
estimating a state of charge (SOC) of the battery; and
estimating the open circuit voltage based on the state of charge.
Resistance state estimation method further comprising a. A program executed by a processor of a battery device and stored in a recording medium,
The program causes the processor to
estimating the internal resistance of the battery based on the measured voltage of the battery, the measured current of the battery and the open circuit voltage of the battery;
estimating the temperature of the battery based on the measured temperature of the battery;
extracting an initial resistance when the battery is in an initial state corresponding to the estimated temperature, and
estimating a resistance state of the battery based on the initial resistance and the internal resistance;
program to run.

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