KR20210127074A - Battery management system and method for determining active material content in electrode of battery - Google Patents

Abstract

The present disclosure relates to a battery management system and method, which determine at least one peak in an inverse-differential capacity analysis curve of a cell, determine at least one peak in an incremental capacity analysis curve associated with the at least one electrode, map at least one peak determined in the inverse-differential capacity analysis curve with at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode, and determine an active material content in at least one electrode of a battery based on mapping.

Description

BATTERY MANAGEMENT SYSTEM AND METHOD FOR DETERMINING ACTIVE MATERIAL CONTENT IN ELECTRODE OF BATTERY

The following embodiments relate to a battery management system, and to a technique for determining an active material content of a battery electrode.

If the battery is supplied by the supplier or service provider and the supplier or service provider only provides the total capacity and rated voltage and operating conditions (power, temperature, etc.) of the battery, the battery management system (BMS) Developing a robust state estimation model for use requires knowing how much lithium can be hosted on the anode and cathode (active material load).

It is also important to track the degradation associated with the degradation of the positive and negative poles as the battery ages.

Therefore, there is a need for techniques to estimate performance degradation with positive and negative electrodes operating in the SOC range (or voltage range), active material load, and battery/cell aging.

The present disclosure aims to provide a battery management system and method for determining the active material content of a battery electrode.

According to an embodiment, a method for determining an active material content of a battery electrode in a battery management system includes: determining at least one peak in an inverse-differential capacity analysis curve of a cell; determining at least one peak in an incremental capacity analysis curve associated with the at least one electrode; mapping at least one peak determined in the inverse-differential capacity analysis curve to at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode; and determining the active material content in at least one electrode of the battery based on the mapping.

In this case, the method of determining an active material content of a battery electrode in the battery management system may further include determining an available capacity corresponding to the at least one electrode as the health state of the battery decreases.

In this case, the step of determining at least one peak in the inverse-differential capacity analysis curve of the cell includes: a state of charge associated with the one or more electrodes when the cell is in a fully discharged state, and the one or more peaks when the cell is in a fully charged state obtaining an incremental capacity analysis curve value obtained from a state of charge associated with the electrode, an inverse-differential capacity analysis curve value associated with a commercial cell, and a difference between anode potential and cathode potential; and the inverse-differential capacity analysis curve of the cell based on the state of charge associated with the at least one electrode when the cell is in a fully discharged state, the state of charge associated with the one or more electrodes when the cell is in a fully charged state , determining the incremental capacity analysis curve value obtained from the difference between the anode potential and the cathode potential using incremental capacity analysis and the inverse-differential capacity analysis curve value associated with the commercial cell.

In this case, the step of determining at least one peak in the incremental capacity analysis curve related to the at least one electrode may include a state of charge associated with the at least one electrode when the cell is in a fully discharged state, and a state of charge associated with the at least one electrode when the cell is in a fully charged state. obtaining an incremental capacity analysis curve value obtained from a state of charge associated with the at least one electrode, an incremental capacity analysis curve value associated with a commercial cell, and a difference between an anode potential and a cathode potential; and one or more peaks in an incremental capacity analysis curve associated with the at least one electrode based on the state of charge associated with the at least one electrode when the cell is in a fully discharged state, the one or more electrodes when the cell is in a fully charged state. and determining an incremental capacity analysis curve value obtained from the difference between the positive electrode potential and the negative electrode potential using incremental capacity analysis and a state of charge associated with a commercial cell.

In this case, the determining of the content of the active material in the at least one electrode of the battery may include: acquiring state-of-charge _{values of SOC a1} and SOC _a2 at potential values of points _{V a1} and V _{a2 ;} obtaining state-of-charge _{values of SOC c1} and SOC _c2 from potential values of points V _c1 and V _{c2 ;} and determining the active material content in the at least one electrode of a battery having a state of charge value of the SOC _a1 , the SOC _a2 , the SOC _c1 and the SOC _{c2 .}

In this case, the step of mapping the at least one peak determined in the inverse-differential capacity analysis curve to the at least one peak determined in the incremental capacity analysis curve related to the at least one electrode includes: an open circuit potential of the positive electrode, an open circuit of the negative electrode obtaining a potential, an open circuit voltage curve of the test cell and an incremental capacity analysis curve of the battery; normalizing the range of states of charge of the positive electrode and the negative electrode from 0 to 1; minimizing the peak difference in the normalized state of charge range of each of the positive electrode and the negative electrode; and mapping the peak value associated with the positive electrode and the peak value associated with the negative electrode based on the minimized peak difference.

In this case, the at least one electrode may be an anode and a cathode.

In this case, the inverse-differential capacity analysis may be obtained by dividing a rate of change of capacity by a rate of change of voltage versus capacity.

In this case, the incremental capacity analysis may be obtained by dividing a rate of change of capacity by a rate of change of voltage versus voltage.

The battery management system according to an embodiment determines at least one peak in an inverse-differential capacity analysis curve of a cell, determines at least one peak in an incremental capacity analysis curve associated with the at least one electrode, and the inverse- mapping at least one peak determined in a differential capacity analysis curve with at least one peak determined in said incremental capacity analysis curve associated with said at least one electrode, said active material content in said at least one electrode of a battery based on said mapping and a processor that determines

In this case, the processor may determine the available capacity corresponding to the at least one electrode as the health state of the battery decreases.

In this case, in the processor, determining at least one peak in the inverse-differential capacity analysis curve of the cell includes: a state of charge associated with the one or more electrodes when the cell is in a fully discharged state; obtaining a state of charge associated with one or more electrodes, an inverse-differential capacity analysis curve value associated with a commercial cell, and an incremental capacity analysis curve value obtained from a difference between an anode potential and a cathode potential, wherein when the cell is in a fully discharged state, the at least one the inverse-differential capacity analysis curve of the cell based on the state of charge associated with an electrode, the state of charge associated with the one or more electrodes when the cell is in a fully charged state, and the inverse-differential capacity analysis curve associated with the commercial cell Value and incremental capacity analysis may be used to determine the value of the incremental capacity analysis curve obtained from the difference between the anode potential and the cathode potential.

In this case, in the processor, determining at least one peak in the incremental capacity analysis curve associated with the at least one electrode includes: a state of charge associated with the at least one electrode when the cell is in a fully discharged state; obtains a state of charge associated with the at least one electrode, an incremental capacity analysis curve value associated with a commercial cell, and an incremental capacity analysis curve value obtained from a difference between an anode potential and a cathode potential, wherein when the cell is in a fully discharged state, the at least one or more peaks in an incremental capacity analysis curve associated with the at least one electrode based on the state of charge associated with one electrode, a state of charge associated with the one or more electrodes when the cell is in a full state of charge, incremental capacity associated with a commercial cell Analytical curve values and incremental capacity analysis can be used to determine an incremental capacity analysis curve value obtained from the difference between the anode potential and the cathode potential.

In this case, in the processor, determining the content of the active material in the at least one electrode of the battery includes: obtaining state-of-charge _{values of SOC a1} and SOC _a2 at potential values of points _{V a1} and V _{a2 , and V c1} and V _c2 obtain the state-of-charge _{values of SOC c1} and SOC _c2 at the potential value of the point, and determine the active material content in the at least one electrode of the battery _{, the state of charge value being the SOC a1} , the SOC _a2 , the SOC _c1 and the SOC _c2 can

In this case, in the processor, the mapping of the at least one peak determined in the inverse-differential capacity analysis curve with the at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode is the open circuit potential of the positive electrode, the negative Obtain the open circuit potential, the open circuit voltage curve of the test cell and the incremental capacity analysis curve of the battery, normalize the state of charge range of the positive electrode and the negative electrode from 0 to 1, and the normalized charge of each of the positive electrode and the negative electrode The peak difference in the state range may be minimized, and the peak value associated with the anode and the peak value associated with the cathode may be mapped based on the minimized peak difference.

In this case, the at least one electrode may be an anode and a cathode.

In this case, the inverse-differential capacity analysis may be obtained by dividing a rate of change of capacity by a rate of change of voltage versus capacity.

In this case, the incremental capacity analysis may be obtained by dividing a rate of change of capacity by a rate of change of voltage versus voltage.

1 is a diagram illustrating a configuration of a battery management system for determining an active material content in a negative electrode and a positive electrode of a battery according to an embodiment.
2A is a flowchart illustrating a process of determining an active material content in a negative electrode and a positive electrode of a battery according to an exemplary embodiment.
FIG. 2B is a flowchart illustrating a process for estimating a range of states of charge of a positive electrode and a negative electrode in relation to FIG. 2A according to an exemplary embodiment.
2C is a flowchart illustrating a process of estimating active material load and deterioration in anode and cathode according to an exemplary embodiment.
3 is a graph illustrating an open circuit potential of an electrode and a battery according to an exemplary embodiment.
4 is an exemplary graph plotting equilibrium potential versus SOC, according to an embodiment.
5 is a graph illustrating equilibrium potential versus normalized electrode state of charge, according to an embodiment.
6 is a graph illustrating a cell equilibrium potential for an estimated state of charge using an electrode open circuit voltage, according to an embodiment.
7 is a graph illustrating peak matching of a dQ/dV versus V plot, according to an embodiment.
8 is a graph illustrating peak mapping of a dQ/dV versus Q plot, according to an embodiment.
9 is a graph illustrating an electrode load calculation according to an exemplary embodiment.
10 is a graph illustrating estimation of anode and cathode capacity loss according to battery aging, according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all modifications, equivalents and substitutes for the embodiments are included in the scope of the rights.

The terms used in the examples are used for description purposes only, and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

In addition, in the description with reference to the accompanying drawings, the same components are given the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In the description of the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

In addition, in describing the components of the embodiment, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only for distinguishing the components from other components, and the essence, order, or order of the components are not limited by the terms. When it is described that a component is “connected”, “coupled” or “connected” to another component, the component may be directly connected or connected to the other component, but another component is between each component. It will be understood that may also be "connected", "coupled" or "connected".

Components included in one embodiment and components having a common function will be described using the same names in other embodiments. Unless otherwise stated, a description described in one embodiment may be applied to another embodiment, and a detailed description in the overlapping range will be omitted.

The present disclosure determines at least one peak in an inverse-differential capacity analysis (dQ/dV vs. Q) curve of a cell, and performs incremental capacity analysis (ICA) associated with at least one electrode. determining at least one peak in the (dQ/dV versus V) curve, mapping the at least one peak determined in the inverse-differential capacity analysis curve to the at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode, and , to a battery management system and method for determining an active material content in at least one electrode of a battery based on the mapping.

Unlike existing methods and systems, the present disclosure can be used to determine the active material loading of the anode and cathode for fresh and degraded cells in a reliable manner.

The present disclosure can be used to accurately determine the active material load and limits of the maximum state of charge (SOC) and minimum state of charge for anode and cathode, which can increase the accuracy of BMS state estimation. .

The present disclosure provides an accurate estimate of the state of health (SOH) of a battery and can be used to predict the capacity decline of the anode and cathode to help understand the degradation mode.

The present disclosure can also be used to determine the usable capacity (or capacity loss) of an individual electrode based on battery life (ie, SOH).

For example, in a battery management system, each active material of an electrode undergoes a phase transition during intercalation and de-intercalation of lithium. ) suffers The phase transition regions of each active material occur at different levels of lithium intercalation/concentration (electrode SOC). In the phase transition region, the active material open circuit potential (OCP) remains nearly flat with the electrode SOC (lithium concentration). The phase shift function can be extracted using incremental capacity analysis (dQ/dV vs. V). An electrode operating in the SOC range can be obtained by matching the characteristics of the estimated battery OCV (= OCP _cathode - OCP _anode ) with the commercial battery open circuit voltage (OCV). An inverse differential capacity analysis (dQ/dV vs. Q) plot of the battery is used to determine how much active material loading is present on each electrode. The anode and cathode peaks are identified in this plot of the inverse differential capacity analysis. The difference in charge (Q) between the two peaks of the anode and the two peaks of the cathode is used to estimate the active material loading. The same procedure can be used to estimate the electrode capacity loss at various levels of battery degradation in a reliable and accurate manner.

Hereinafter, a battery management system and method for determining an active material content of a battery electrode according to an embodiment of the present disclosure will be described in detail with reference to FIGS. 1 to 9 .

1 is a diagram illustrating a configuration of a battery management system 100 for determining the content of an active material in a negative electrode and a positive electrode of a battery according to an embodiment.

Referring to FIG. 1 , the battery management system 100 includes a processor 110 , a communicator 120 , and a memory 130 . The processor 110 is coupled to the communicator 120 and the memory 130 . The processor 110 includes an active substance content determination engine 110a and an SOH determination engine 110b.

The battery management system 100 is implemented in electronic devices and electric vehicles. Electronic devices include, for example, cell phones, smart phones, personal digital assistants (PDAs), wireless modems, tablet computers, laptop computers, wireless local loop (WLL) stations, universal serial bus (USB) dongles. (dongle), Internet of Things (IoT), and the like.

The processor 110 is configured to estimate or determine a positive electrode and a negative electrode operating SOC range.

In one embodiment, the active material content determination engine 110a is a positive OCP (eg, 320 in FIG. 3 ), a negative OCP (eg, 330 in FIG. 3 ), an experimental cell OCV curve (eg, FIG. 3 ). 310 of 3), and cell dQdV versus V (eg, 740 in FIG. 7 ) using incremental dose analysis.

In addition, the active material content determination engine 110a is configured to normalize the two electrode SOC range from 0 to 1 as in the example below. At the anode, the SOC range may be normalized from 0.3-0.98 (eg, 410 in FIG. 4 ) to 0.0-1.0 (eg, 510 in FIG. 5 ). At the cathode, the SOC range may be normalized from 0.0-0.97 (eg, 420 in FIG. 4 ) to 0.0-1.0 (eg, 520 in FIG. 5 ).

Further, the active material content determination engine 110a is configured to initialize _{SOC 0} and SOC _{1 of the two electrodes for the cell model.} Here, SOC ₀ represents an OCP value at _{SOC 0, and SOC 1} represents an OCP value at SOC 1.

In addition, the active substance content determination engine 110a uses incremental capacity analysis (ICA) to output the model, OCV versus SOC curve (eg, the estimate at 730 in FIG. 7 ), dQdV versus V (eg, For example, the estimation at 740 of FIG. 7 ). Further, the active substance content determination engine 110a is configured to determine the difference in dQdV versus V peak positions from the experimental data. In addition, the active material content determination engine 110a is configured to perform optimization on _{SOC 0} and SOC _{1 to minimize the peak difference.}

Further, the active substance content determination engine 110a may be configured to verify that a convergence point is met. The active material content determination engine 110a may stop the active material content determination process when the convergence point is satisfied, _{and update SOC 0} and SOC ₁ of the two electrodes if the convergence point is not met.

3 is a graph illustrating an open circuit potential of an electrode and a battery according to an exemplary embodiment.

Referring to FIG. 3 , reference numeral 310 is a graph showing an OCV of an N9 battery for an experiment. 320 is a graph showing the LCO electrode. 330 is a graph showing a graphite electrode.

4 is a graph illustrating an equilibrium potential versus SOC, according to an embodiment.

5 is a graph illustrating equilibrium potential versus normalized electrode state of charge, according to an embodiment.

6 is a graph illustrating a cell equilibrium potential for an estimated state of charge using an electrode open circuit voltage, according to an embodiment.

Cell OCV can be obtained using <Equation 1> and SOC _norm can be obtained using <Equation 2>.

[Equation 1]

Here, Cell OCV represents the open circuit voltage of a cell of the battery, Cathode OCP represents the open circuit potential of the cathode, and Anode OCP represents the open circuit potential of the anode.

[Equation 2]

Here, SOC _min represents the minimum value of the state of charge, and SOC _max represents the maximum value of the state of charge.

7 is a graph illustrating peak matching of a dQ/dV versus V plot, according to an embodiment.

Referring to FIG. 7 , 710 is a graph showing OCV of a commercial battery. 720 is the estimated OCV from the electrode. 730 is a graph showing a comparison between the estimated cell OCV and the commercial battery OCV after optimization of the electrode SOC value. 740 is a graph showing peak comparison of dQ/dV vs. V plots of estimated and commercial batteries.

8 is a graph illustrating peak mapping of a dQ/dV versus Q plot, according to an exemplary embodiment.

Referring to FIG. 8 , 810 is a graph showing a dQ/dV versus V plot of natural graphite (NG). 820 is a graph showing a dQ/dV versus V plot of Lithium Cobalt Oxide (LCO). 830 is a graph showing a dQ/dV vs. V plot (commercial battery). 840 is a graph showing a dQ/dV vs. Q plot (commercial battery).

In one embodiment, the peak mapping of the dQ/dV vs. Q plot may be obtained based on the steps below.

Step 1: Identification of the anode and cathode peaks in the dQ/dV vs. V plot of a commercial battery can be made by mapping the peaks to individual electrode dQ/dV vs. V plot peaks.

Step 2: The anode and cathode peaks in the dQ/dV vs. V plot of a commercial battery are mapped to a dQ/dV vs. Q plot of the commercial battery for capacity estimation between the two peaks of each electrode.

Step 3: Anode and cathode peak voltage values can be obtained from 810 and 820 of FIG. 8, respectively, and are shown in <Table 1>.

Step 4: The anode and cathode peak capacity values can be estimated at 830 and 840 in FIG. 8, respectively (referred to in <Table 1>).

electrode Q1 (mAh) Q2 (mAh) V1 (V) V2 (V) anode 365 1297 0.133 0.218 cathode 3097 2664 4.171 4.061

9 is a graph illustrating an electrode load calculation according to an exemplary embodiment.

Referring to FIG. 9 , reference numeral 910 is a graph illustrating an OCP plot of natural graphite (NG), and reference numeral 920 is a graph illustrating an OCP plot of lithium cobalt oxide (LCO).

In one embodiment, the electrode load calculation may be determined based on the following steps.

Step 1: The anode and cathode peak at voltage values obtained from 910 and 920 in FIG.

Step 2: The SOC values of the anode and the cathode corresponding to the voltage may be obtained from 910 and 920 of FIG. 9 , respectively.

Step 3: The ΔSOC value of the anode is equal to the difference between the anode peak SOC values. It can be estimated using <Equations 4a and 4b>. Similarly, the Δ SOC value of the cathode can be estimated.

Step 4: The (ΔQ) _cell for the anode equals the capacity difference between the peak values. (ΔQ) _cell can be estimated using <Equations 3a and 3b>. The (ΔQ) _cell for the cathode can also be estimated in a similar way.

Step 5: The active material loads of the anode and cathode of a commercial battery can be obtained using Equation (5).

The anode and cathode peak voltages, SOC values, and corresponding capacity values for commercial batteries are shown in Table 2.

Q-1 (mAh) Q-2 (mAh) V-1(V) V-2(V) SOC-1 SOC-2 anode 365 1297 0.133 0.218 0.312 0.089 cathode 3097 2664 4.171 4.061 0.245 0.343

[Equation 3a]

Here, (ΔQ) _cell represents the capacity difference between the two peaks, Q _c1 and Q _c2 represent the respective values of the two peaks (c ₁ , c ₂ ) of the cathode, and abs() represents the absolute value function. .

[Equation 3b]

Here, (ΔQ) _cell represents the capacity difference between the two peaks, Qa ₁ and Qa ₂ represent the respective values in the two peaks (a ₁ , a ₂ ) of the anode, and abs() represents the absolute value function. .

[Equation 4a]

Here, (ΔSOC)electrode represents the difference between the two peak SOC values, SOC _c1 and SOC _c2 represent the respective SOC values at the two peaks (c ₁ , c ₂ ) of the cathode, and abs() is an absolute value function. indicates

[Equation 4b]

Here, (ΔSOC)electrode represents the difference between the two peak SOC values, SOCa ₁ and SOCa ₂ represent the respective SOC values at the two peaks (a ₁ , a ₂ ) of the anode, and abs() is an absolute value function. indicates

[Equation 5]

Also, the electrodes lose capacity during operation and cycling of the cell at different rates.

In the case of the present disclosure, the capacity of individual electrodes may be checked at different aging levels.

Information regarding the capacity of individual electrodes can be used to predict the usable capacity of the cell and to model individual electrode degradations using the SOH decision engine 110b.

In one embodiment, the active material content determination engine 110a is configured _{to obtain a dQdV versus V curve obtained from the difference between the SOC 0} and SOC ₁ of both the positive and negative electrodes, the dQdV versus V curve of a commercial cell, and the positive and negative potentials. may be configured (eg, 830 in FIG. 8 ).

In addition, the active material content determination engine 110a calculates the incremental capacity analysis (dQdV vs. V) curves of both the positive electrode (eg, obtained at 820 in FIG. 8 ) and the negative electrode (eg, obtained at 810 in FIG. 8 ) curves and It can be configured to estimate the dQdV versus Q curve of a commercial cell using the inverse differential capacitance (eg, obtained at 840 of FIG. 8 ).

In addition, the active material content determination engine 110a maps the peaks of the commercial cell to the positive (c1, c2) (820 in FIG. 8) and negative (a1, a2) (810 in FIG. 8) peaks, and similarly, Mapping the peaks between the cell's dQdV vs. V (830 in Fig. 8) and dQdV vs. Q (840 in Fig. 8) is constructed to identify positive and negative peaks at dQdV vs. V (810 in Fig. 8) of the commercial cell.

Further, the active substance content determination engine 110a is configured to estimate the following information.

For example, the potential value (x-axis) of point _{a 1} is recorded _{as V a1} in 810 of FIG. 8 and a ₂ is recorded as V _a2 , and the potential value (x-axis) of point _{c 1 is V in 820 of FIG. 8 .} as _c1, 840 of the recording to the V _c2, and in Fig. 8 _{c 2 a 1, a 2,} c 1, the capacitance (x-axis) at the point c ₂ respectively, _{Q a1, Q a2, Q c1} , Record as Q _c2.

In addition, the active material content determination engine 110a _{estimates the following potential values V a1} and V _{a2 in 810 of FIG. 8 , and corresponding SOC values such as SOC a1} and SOC _a2 in 910 of FIG. 9 , 820 in FIG. 8 . It is used to obtain the potential values of the points _{V c1} and V _{c2 in} , and is configured to be used to obtain corresponding SOC values corresponding to SOCs and values such as SOC _c1 and SOC _{c2 in 920 of FIG. 9 .}

)을 사용하여 음극의 활성 물질 부하를 결정하도록 구성된다.In addition, the active substance content determination engine 110a is

) to determine the active material loading of the negative electrode.

)을 사용하여 양극의 활성 물질 부하를 결정하도록 구성된다.In addition, the active substance content determination engine 110a is

) to determine the active material loading of the positive electrode.

10 is a graph illustrating estimation of anode and cathode capacity loss according to battery aging, according to an embodiment.

Referring to FIG. 10 , it can be seen how dQ/dV vs. Q of a commercial battery plot changes at various cycle numbers. Points a ₁ and a ₂ correspond to the anode, and points c ₁ and c ₂ correspond to the cathode, respectively. Q _cycle and the cathode of the Q _cycle of the anode can be estimated using each <Equation 6> with <Equation 7>. _{The Q cycle} of a commercial battery is equal to the final dQ/dV versus Q curve. Q ₀ is the first cycle capacity. The capacity loss percentage of the anode, the cathode, and the commercial battery with cycle numbers can be estimated using Equation (8).

[Equation 6]

[Equation 7] (ΔSOC) electrode represents the difference between the two peak SOC values, SOC _c1 and SOC _c2 represent the respective SOC values at the two peaks (c ₁ , c _{2 ) of the cathode, and abs() is} Represents an absolute value function.

[Equation 8]

number of cycles
(Cycle No) Cell
(% Cap Loss) Anode
(% Cap Loss) cathode
(% Cap Loss) One 0 0 0 216 4.69 4.93 3.48 376 8.66 7.23 6.65 409 14.87 15.34 11.07

Table 3 is an exemplary table of anode and cathode and cell capacity loss percentages according to cycle number.

In addition, the processor 110 is configured to execute instructions stored in the memory 130 and perform various processes.

Communicator 120 is configured to communicate internally between internal hardware components and external devices via one or more networks.

Memory 130 also stores instructions to be executed by processor 110 . Memory 130 may include a non-volatile storage element. Examples of such non-volatile storage elements may include magnetic hard disks, optical disks, floppy disks, flash memory or in the form of electrical programmable memory (EPROM) or electrical erasable and programmable (EEPROM) memory. Also, memory 130 may be considered a non-transitory storage medium in some examples. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transient” should not be construed as meaning that the memory 130 is immobile. In some examples, memory 130 may be configured to store a greater amount of information than memory. In certain instances, the non-transitory storage medium may store data that may change over time (eg, in random access memory (RAM) or cache).

1 illustrates various hardware components of the battery management system 100 , it should be understood that other embodiments are not limited thereto. In other embodiments, the battery management system 100 may include fewer or more components. In addition, labels or names of components are used for illustrative purposes only and do not limit the scope of the present disclosure. One or more components may be combined together to perform the same or substantially similar function to determine the active material content in the negative and positive electrodes of the battery.

Hereinafter, a method of determining the content of an active material in a battery electrode in the battery management system according to the present disclosure configured as described above will be described with reference to the drawings.

2A is a flowchart illustrating a process of determining an active material content in a negative electrode and a positive electrode of a battery according to an exemplary embodiment.

Referring to FIG. 2A , steps 202a - 208a are performed by the processor 110 .

The processor 110 determines ( 202a ) at least one peak in an inverse-differential capacity analysis (dQ/dV vs. Q) curve of the cell.

Then, the processor 110 determines at least one peak in the incremental capacity analysis (dQ/dV versus V) curve associated with the positive electrode and the negative electrode (204a).

Then, the processor 110 maps the at least one determined peak in the inverse-differential capacity analysis curve with the at least one determined peak in the incremental capacity analysis curve associated with the positive electrode and the negative electrode (206a).

Then, the processor 110 determines the active material content in the negative electrode and the positive electrode of the battery (208a).

FIG. 2B is a flowchart illustrating a process for estimating a range of states of charge of a positive electrode and a negative electrode with respect to FIG. 2A according to an exemplary embodiment.

Referring to FIG. 2B , steps 202b - 216b are performed by the processor 110 .

Processor 110 obtains positive OCP (eg, FIG. 3B ) and negative OCP (eg, 330 in FIG. 3 ), experimental cell OCV curve (eg, 310 in FIG. 3 ), and incremental capacity analysis A cell dQdV versus V (eg, 740 in FIG. 7 ) is obtained using incremental capacity analysis (ICA) ( 202b ).

Then, the processor 110 normalizes the two electrode SOC range from 0 to 1 as follows (204b).

At the anode, the SOC range may be normalized from 0.3-0.98 (eg, 410 in FIG. 4 ) to 0.0-1.0 (eg, 510 in FIG. 5 ).

At the cathode, the SOC range may be normalized from 0.0-0.97 (eg, 420 in FIG. 4 ) to 0.0-1.0 (eg, 520 in FIG. 5 ).

_{Then, the processor 110 initializes SOC 0} and SOC ₁ of the two electrodes for the cell model ( 206b ).

Then, the processor 110 uses the model output OCV versus SOC curve (eg, estimation of 730 in FIG. 7 ) and ICA to obtain dQdV versus V (eg, estimation of 740 in FIG. 7 ) ( 208b ). ).

Then, the processor 110 finds a difference between the dQdV and V peak positions in the experimental data (210b).

Then, the processor 110 minimizes the peak difference by performing optimization on _{SOC 0} and SOC _{1 ( 212b ).}

Then, the processor 110 determines whether a convergence point is satisfied (214b).

If the convergence point is satisfied as a result of the check in step 214b, the processor 110 stops the present algorithm.

If the convergence point is not satisfied as a result of the check in step 214b, the processor 110 _{updates SOC 0} and SOC ₁ of the two electrodes (216b).

2C is a flowchart illustrating a process of estimating active material load and deterioration in anode and cathode according to an exemplary embodiment.

Referring to FIG. 2C , steps 202c - 214c are performed by the processor 110 .

_{Processor 110 obtains SOC 0} and SOC ₁ of both positive and negative electrodes, dQdV versus V curve of a commercial cell, and a dQdV versus V curve (eg, 830 in FIG. 8 ) from the difference in positive and negative potentials from FIG. 2B . do (202c).

Then, the processor 110 estimates the dQdV versus V curve of both the positive electrode (eg, 820 in FIG. 8 ) and the negative electrode (eg, 810 in FIG. 8 ) using incremental capacity analysis (ICA), and inverse - Estimate the dQdV vs. Q curve of the commercial cell (eg, 840 in FIG. 8 ) using differential capacity analysis (204c).

Then, the processor 110 maps the peaks of the commercial cell to the positive (c1, c2) (820 in FIG. 8) and negative (a1, a2) (810 in FIG. 8) peaks, and similarly, the dQdV versus dQdV of the commercial cell The peaks between V (830 in Fig. 8) and dQdV vs. Q (840 in Fig. 8) are mapped to identify positive and negative peaks at dQdV vs. V in a commercial cell (810 in Fig. 8) (206c).

Then, the processor 110 estimates the following information (208c). For example, the potential value (x-axis) of point _{a 1} is recorded _{as V a1} in 810 of FIG. 8 and a ₂ is recorded as V _a2 , and the potential value (x-axis) of point _{c 1 is V in 820 of FIG. 8 . Record as c1} and c ₂ as V _c2 , and the capacity values (x-axis) at points _{a 1} , a ₂ , c ₁ , and c ₂ of 840 of FIG. 8 _{are Q a1} , Q _a2 , Q _c1 , Q _{c2 , respectively.} record as

Then, the processor 110 _{is used to obtain the corresponding SOC values such as SOC a1} and SOC _a2 in 910 of FIG. 9 , the potential values of the points Va1 and Va2 in 810 of FIG. 8 , SOC _c1 and SOC _{c2 in 920 of FIG. 9 . The potential values of the points V c1} and V _c2 in 820 of FIG. 8 used to obtain the corresponding SOC values such as 210c are estimated ( 210c ).

)을 사용하여 음극의 활성 물질 부하를 결정한다(212c).And, the processor 110 is the equation (

) to determine the active material load of the negative electrode (212c).

)을 사용하여 양극의 활성 물질 부하를 결정한다(214c).And, the processor 110 is the equation (

) to determine the active material load of the positive electrode (214c).

Unlike conventional methods, the present disclosure can be used to determine the active material load of each electrode that can be used in on-board state estimation procedures. The present disclosure can be used to estimate the SOC range of both anode and cathode. The present disclosure can be used to estimate the active material loading at the negative and positive electrodes of commercial batteries without applying destructive methods. The present disclosure is non-destructive and can be used to estimate electrode degradation with battery life. The electrode degradation information in this disclosure can help to tune the operating voltage (or SOC) window and guide the design of the anode and cathode in a better way for extended life. The present disclosure requires only minimal information about commercial batteries (electrode materials). The present disclosure can be applied to various levels of battery aging to accurately determine the capacity loss of individual electrodes. This can help users predict the correct state of the battery. It also aids in better electrode design and material selection.

The present disclosure utilizes the measured currents, voltages, and capacities of individual electrodes and commercial batteries to obtain characteristic curves of dQ/dV vs. Q and dQ/dV vs. V to determine the electrode's operating SOC range, loading of active materials across both anode and cathode. and it is estimated that capacity decreases with battery age. The various acts, acts, blocks, steps, etc., of the flowcharts 200a - 200c may be performed in the order presented, in a different order, or concurrently.

In addition, in some embodiments, some of the operations, operations, blocks, steps, etc. may be omitted, added, modified, skipped, etc. without departing from the scope of the present disclosure.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer readable medium may store program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and carry out program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems, and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (18)

determining at least one peak in an inverse-differential capacity analysis curve of the cell;
determining at least one peak in an incremental capacity analysis curve associated with the at least one electrode;
mapping at least one peak determined in the inverse-differential capacity analysis curve to at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode; and
determining the active material content in at least one electrode of a battery based on the mapping;
A method for determining the active material content of a battery electrode in a battery management system comprising:
According to claim 1,
determining a usable capacity corresponding to the at least one electrode as the health state of the battery decreases;
A method for determining an active material content of a battery electrode in a battery management system further comprising:
According to claim 1,
Determining at least one peak in the inverse-differential capacity analysis curve of the cell comprises:
a state of charge associated with the one or more electrodes when the cell is in a fully discharged state, a state of charge associated with the one or more electrodes when the cell is in a fully charged state, an inverse-differential capacity analysis curve value associated with a commercial cell and positive potential versus negative electrode obtaining incremental dose analysis curve values obtained from differences between potentials; and
the inverse-differential capacity analysis curve of the cell based on the state of charge associated with the at least one electrode when the cell is in a fully discharged state, the state of charge associated with the one or more electrodes when the cell is in a fully charged state; determining the value of the incremental capacity analysis curve obtained from the difference between the anode potential and the cathode potential using the inverse-differential capacity analysis curve value associated with the commercial cell and the incremental capacity analysis;
A method for determining the active material content of a battery electrode in a battery management system comprising:
According to claim 1,
Determining at least one peak in the incremental capacity analysis curve associated with the at least one electrode comprises:
a state of charge associated with the at least one electrode when the cell is in a fully discharged state, a state of charge associated with the at least one electrode when the cell is in a fully charged state, an incremental capacity analysis curve value associated with a commercial cell and positive potential versus negative electrode obtaining incremental dose analysis curve values obtained from differences between potentials; and
one or more peaks in an incremental capacity analysis curve associated with the at least one electrode based on the state of charge associated with the at least one electrode when the cell is in a fully discharged state, the one or more electrodes when the cell is in a fully charged state; determining an associated state of charge, an incremental capacity analysis curve value associated with a commercial cell, and an incremental capacity analysis curve value obtained from the difference between the positive electrode potential and the negative electrode potential using incremental capacity analysis;
A method for determining the active material content of a battery electrode in a battery management system comprising:
According to claim 1,
Determining the active material content in at least one electrode of the battery comprises:
obtaining state-of-charge _{values of SOC a1} and SOC _a2 from potential values of points V _a1 and V _{a2 ;}
obtaining state-of-charge _{values of SOC c1} and SOC _c2 from potential values of points V _c1 and V _{c2 ;} and
determining the active material content in the at least one electrode of a battery having a state of charge value of the SOC _a1 , the SOC _a2 , the SOC _c1 and the SOC _{c2 .}
A method for determining the active material content of a battery electrode in a battery management system comprising:
According to claim 1,
mapping the at least one peak determined in the inverse-differential capacity analysis curve to the at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode,
obtaining an open circuit potential of an anode, an open circuit potential of a cathode, an open circuit voltage curve of the test cell, and an incremental capacity analysis curve of the battery;
normalizing the range of states of charge of the positive electrode and the negative electrode from 0 to 1;
minimizing the peak difference in the normalized state of charge range of each of the positive electrode and the negative electrode; and
mapping the peak value associated with the positive electrode and the peak value associated with the negative electrode based on the minimized peak difference;
A method for determining the active material content of a battery electrode in a battery management system comprising:
According to claim 1,
the at least one electrode,
positive and negative
A method for determining the active material content of a battery electrode in a battery management system.
According to claim 1,
The inverse-differential capacity analysis is,
The rate of change of capacity divided by the rate of change of voltage versus capacity
A method for determining the active material content of a battery electrode in a battery management system.
According to claim 1,
The incremental dose analysis is
The rate of change of capacity divided by the rate of change of voltage versus voltage
A method for determining the active material content of a battery electrode in a battery management system.
determining at least one peak in an inverse-differential capacity analysis curve of a cell, determining at least one peak in an incremental capacity analysis curve associated with said at least one electrode, and determining at least one peak determined in said inverse-differential capacity analysis curve a processor for mapping at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode and determining the active material content in the at least one electrode of a battery based on the mapping
A battery management system comprising a.
11. The method of claim 10,
The processor is
and determining the available capacity corresponding to the at least one electrode as the health state of the battery decreases.
battery management system.
11. The method of claim 10,
Determining in the processor at least one peak in an inverse differential capacity analysis curve of the cell comprises:
a state of charge associated with the one or more electrodes when the cell is in a fully discharged state, a state of charge associated with the one or more electrodes when the cell is in a fully charged state, an inverse-differential capacity analysis curve value associated with a commercial cell and positive potential versus negative electrode obtaining the incremental dose analysis curve values obtained from the difference between the potentials,
the inverse-differential capacity analysis curve of the cell based on the state of charge associated with the at least one electrode when the cell is in a fully discharged state, the state of charge associated with the one or more electrodes when the cell is in a fully charged state; determining the incremental capacity analysis curve value obtained from the difference between the positive potential and the negative potential using the inverse-differential capacity analysis curve value and the incremental capacity analysis associated with the commercial cell;
battery management system.
11. The method of claim 10,
determining, in the processor, at least one peak in an incremental capacity analysis curve associated with the at least one electrode,
a state of charge associated with the at least one electrode when the cell is in a fully discharged state, a state of charge associated with the at least one electrode when the cell is in a fully charged state, an incremental capacity analysis curve value associated with a commercial cell and positive potential versus negative electrode obtaining the incremental dose analysis curve values obtained from the difference between the potentials,
one or more peaks in an incremental capacity analysis curve associated with the at least one electrode based on the state of charge associated with the at least one electrode when the cell is in a fully discharged state, the one or more electrodes when the cell is in a fully charged state; determining the associated state of charge, the incremental capacity analysis curve value associated with a commercial cell, and the incremental capacity analysis curve value obtained from the difference between the positive electrode potential and the negative electrode potential using incremental capacity analysis;
battery management system.
11. The method of claim 10,
Determining the active material content in at least one electrode of the battery in the processor comprises:
obtain the state of charge _{values of SOC a1} and SOC _a2 from the potential values of the points V _a1 and V _{a2 ,}
obtain the state-of-charge _{values of SOC c1} and SOC _c2 from the potential values of the points V _c1 and V _{c2 ,}
determining the active material content in the at least one electrode of a battery having a state of charge value of the SOC _a1 , the SOC _a2 , the SOC _c1 and the SOC _c2
battery management system.
11. The method of claim 10,
mapping, in the processor, the at least one peak determined in the inverse-differential capacity analysis curve to the at least one peak determined in the incremental capacity analysis curve associated with the at least one electrode,
obtain the open circuit potential of the positive electrode, the open circuit potential of the negative electrode, the open circuit voltage curve of the test cell and the incremental capacity analysis curve of the battery;
Normalizing the range of states of charge of the positive electrode and the negative electrode from 0 to 1,
Minimizing the peak difference in the normalized state of charge range of each of the positive electrode and the negative electrode,
mapping the peak value associated with the positive electrode and the peak value associated with the negative electrode based on the minimized peak difference
battery management system.
11. The method of claim 10,
the at least one electrode,
positive and negative
battery management system.
11. The method of claim 10,
The inverse-differential capacity analysis is,
The rate of change of capacity divided by the rate of change of voltage versus capacity
battery management system.
11. The method of claim 10,
The incremental dose analysis is
The rate of change of capacity divided by the rate of change of voltage versus voltage
battery management system.

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