KR102483644B1 - Method and apparatus of predicting capacity fade rate of a rechargeable battery - Google Patents

Abstract

A method and apparatus for predicting the capacity degradation rate of a rechargeable battery are disclosed. A method for predicting a capacity degradation rate of a battery according to an embodiment includes collecting capacity degradation data of a battery regarding SOC and current for a predefined number of cycles, generating a capacity degradation model using the capacity degradation data, and estimating a capacity degradation rate of the battery using the capacity degradation model.

Description

Method and apparatus for predicting capacity decline rate of rechargeable battery

The embodiments below relate to a method and apparatus for predicting a rate of capacity degradation of a rechargeable battery.

The massive development and use of stored energy in the electric vehicle market forces the use of rechargeable batteries with high volume densities of stored energy and exhibiting many charge and discharge cycles. The use of rechargeable batteries reduces cost and also provides a low environmental impact. One type of rechargeable battery is a Li-ion based battery and is mainly used in electric vehicles and hybrid electric vehicles. However, the capacity of Li-ion based batteries decreases over time. This is called charge-discharge cycling. In Li-ion based batteries, capacity loss occurs as a result of several mechanisms such as Li-loss, active material loss, electrolyte degradation, and the like. In modern high energy composite cathode Li-ion cells, cathode degradation is considered more important than conventional anode degradation while calculating capacity loss. In general, Li-ion based batteries exhibit slow degradation over thousands of cycles or more. This slow capacity fade usually results from irreversible electrochemical processes that progressively compete with reversible lithium intercalation in the electrodes.

It is necessary to create a mathematical model to predict the capacity fade rate and life expectancy of a battery. One of the mathematical models considers a physics based degradation model for certain components known in Li-ion based batteries. In particular, during charging of Li-ion based batteries, the fundamental source of capacity degradation in Li-ion batteries is caused by loss of Li to solid electrolyte interphase (SEI). Physics-based degradation models can account for this SEI formation at the anode, and also account for electrolyte decomposition and active material loss at the cathode to predict battery life. However, the physics-based degradation model is difficult to implement in a battery management system (BMS) due to uncertain parameters and requires high computational cost.

Other mathematical models use data based models that use regression/machine learning techniques to predict battery life. However, this data-driven model requires a large amount of data that can take years even with multiple testing stations.

In both models, capacity fade rate investigation is performed using a known or fixed charge-discharge cycle at a controlled temperature.

It is impossible to predict capacity loss under arbitrary (e.g., user specific) usage patterns, variable discharge rates, and defective (or broken) cycles, and under most operating conditions condition).

There is a need for a new model to predict the capacity degradation rate or state of health (SOH) of Li-ion based batteries independent of temperature.

Embodiments may provide a technique for predicting a capacity degradation rate of a rechargeable battery using a capacity degradation model.

A method for predicting a capacity degradation rate of a battery according to an embodiment includes collecting capacity degradation data of a battery regarding a state of charge (SOC) and current for a predefined number of cycles, and using the capacity degradation data. and generating a capacity fade model by using the capacity fade model, and estimating a capacity fade rate of the battery using the capacity fade model.

The capacity degradation model is defined as the logarithmic difference in fractional capacity after cycling between different SOCs divided by the difference between the SOCs multiplied by the number of cycles, where the fractional capacity is the cell capacity at the end of cycling versus the initial cell It can be defined as a percentage of capacity (initial cell capacity).

The battery includes a nickel-cadmium battery, a nickel-metal hydride battery, a lithium-ion battery, a lithium sulfur battery, and a thin film battery. battery), a carbon foam-based lead acid battery, a potassium-ion battery, and a sodium-ion battery. there is.

The predefined number of cycles may be determined based on maximum allowable degradation conditions.

The capacity degradation data is collected according to changes in conditions, the conditions being a first condition in which the degradation during charge and discharge cycles is the same and the degradation in which the charging current is constant is different during the charge and discharge cycles. A second condition may be included.

The capacity decrease rate may be notified through an application of a user's mobile phone.

An apparatus according to an embodiment uses a multi-channel battery cycler that collects capacity degradation data of a battery regarding state of charge (SOC) and current for a predefined number of cycles, and the capacity degradation data. and a capacity fade modeler for generating a capacity fade model by using the capacity fade model, and a controller for estimating a capacity fade rate of the battery using the capacity fade model.

The capacity degradation model is defined as the logarithmic difference in fractional capacity after cycling between different SOCs divided by the difference between the SOCs multiplied by the number of cycles, where the fractional capacity is the cell capacity at the end of cycling versus the initial cell It can be defined as a percentage of capacity (initial cell capacity).

The battery includes a nickel-cadmium battery, a nickel-metal hydride battery, a lithium-ion battery, a lithium sulfur battery, and a thin film battery. battery), a carbon foam-based lead acid battery, a potassium-ion battery, and a sodium-ion battery. there is.

The predefined number of cycles may be determined based on maximum allowable degradation conditions.

The capacity degradation data is collected according to changes in conditions, the conditions being a first condition in which the degradation during charge and discharge cycles is the same and the degradation in which the charging current is constant is different during the charge and discharge cycles. A second condition may be included.

The capacity decrease rate may be notified through an application of a user's mobile phone.

1 is a flowchart illustrating a method for predicting a capacity degradation rate of a battery according to an exemplary embodiment.
2 is a schematic block diagram of a system for predicting a capacity degradation rate of a battery according to an embodiment.
3A and 3B show examples of graphical representations of the results of capacity degradation data of a rechargeable battery obtained using a capacity degradation model.
4A to 4E show validation results of capacity degradation rates of rechargeable batteries obtained for different voltage ranges.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be modified and implemented in various forms. Therefore, the embodiments are not limited to the specific disclosed form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Although terms such as first or second may be used to describe various components, such terms should only be construed for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element.

It should be understood that when an element is referred to as being “connected” to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle.

Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, operation, component, part, or combination thereof exists, but one or more other features or numbers, It should be understood that the presence or addition of steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. 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 unless explicitly defined in this specification, it should not be interpreted in an ideal or excessively formal meaning. don't

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these examples. Like reference numerals in each figure indicate like elements.

1 is a flowchart illustrating a method for predicting a capacity degradation rate of a battery according to an exemplary embodiment.

Referring to FIG. 1 , an embodiment describes a method for predicting the capacity fade rate of a rechargeable battery, such as a Li-ion battery. Capacity degradation is due to many factors such as solid electrolyte interphase (SEI) growth at the anode, active material loss at the cathode, and electrolyte decomposition. In an embodiment, capacity degradation can be predicted as a function of state of charge (SOC) and current (I). For example, the capacity of cell C may be defined as in Equation 1.

From Equation 1 above, it can be deduced that the capacitance loss is greater in a large SOC range and/or high current. Since it can be easily implemented through the Arrhenius equation, the effect of temperature can be neglected. That is, the capacity degradation rate can be predicted in terms of SOC and current (I).

In step 102, capacity degradation data of a rechargeable battery, such as a Li-ion battery, in terms of SOC and current is measured under predefined conditions and a predefined number of cycles. can be collected during

For example, a predefined number of cycles may be determined based on maximum allowable degradation conditions. The deterioration condition may include a first condition and a second condition. The first condition may be the assumption that degradation is the same during charge and discharge cycles. The second condition may be the assumption that the degradation is different during charge and discharge cycles in which the charging current is constant.

Deterioration of the Li-ion cell of the total capacity (C) is a function of SOC and current (I), both SOC and current (I) are independent, and the capacity of the cell can be given as Equation 2.

Equation 3 is as follows, and Equation 4 can be obtained.

Equation 5 may be obtained by integrating Equation 4 with respect to a constant current during charge-discharge cycles from the nth cycle to the n+1th cycle based on the first condition.

Here, G ₁ (soc) and f ₃ (I) can be expressed as Equations 6 and 7.

The right-hand sides (RHS) of Equation 5 are constant.

The second condition may be regarded as calculating the capacity decrease rate, and may be calculated as in Equations 8, 9 and 10.

은 수학식 11과 같을 수 있다.here,

may be the same as Equation 11.

The right-hand sides (RHS) of Equation 10 are constant.

Thus, for all conditions, Equation 12 can be obtained after 'n' cycles.

을 재구성하는 실험에서 다양할 수 있다.The maximum voltage is

can be varied in the experiment to reconstruct.

및

을 고려할 수 있다. 수학식 13 및 14를 사용하여, 수학식 15에 적용하면, 수학식 16이 얻어질 수 있다.Two different values expressed as Equation 13 and Equation 14

and

can be considered. Applying Equations 13 and 14 to Equation 15, Equation 16 can be obtained.

Equation 16 may be a model generated using capacity degradation data.

In order to collect the data, it should be considered that the capacity degradation data for n cycles needs to be collected according to the change of the two conditions.

Minimum SOC (e.g. voltage) cutoff (SOC ¹ , SOC ² , ..., SOC ^p )

Cycling current (I ¹ , I ² , ..., I ^q )

The number of cycles (n) may be determined based on maximum allowable degradation conditions. For example, if a cell cycles at its maximum allowable current (I ^max ) in the maximum SOC range (0-1), the cell will reach the end of its life capacity (eg, 80% of its new cell capacity) after 100 cycles. %) can be reached. At that time, n = 100 may be.

Since the effect of current is expected to be high at high discharge rates, for a good estimation of the degradation function, the current range is divided into 5 values (e.g., q~5). While can be covered, 10 values of minimum SOC (eg, p~10) should be considered. For example, all this data can be collected in 3-4 months using a 25-channel battery cycler. Using the collected data, a capacity fade model can be created to predict the rate of capacity fade. This may be done in step 104.

At step 106, the rate of capacity fade for the Li-ion based battery may be estimated using the generated capacity fade model. The resulting capacity fading model can determine the rate of capacity fading for any arbitrary usage cycle.

2 is a schematic block diagram of a system for predicting a capacity degradation rate of a battery according to an embodiment.

Referring to FIG. 2 , a system 200 includes an apparatus 201 for predicting a rate of degradation of a Li-ion battery and an apparatus 208 in which the Li-ion battery is included.

The device 201 may include a multichannel battery cycler 252 , a capacity degradation modeler 254 , and an onboard controller 203 . Device 208 may be a mobile phone or electric vehicle with a Li-ion battery.

The onboard controller unit 203 may include a battery controller chip 206 . The function of each component is explained in detail.

Multichannel battery cycler 202 can repeatedly perform charge-discharge cycles on a given battery at different voltage ranges to verify that the battery meets or exceeds a claimed cycle life.

During charge and discharge cycles, the multichannel battery cycler 202 may collect capacity degradation data of the battery in terms of SOC and current for a predefined number of cycles. For example, one cycle may mean complete charging and discharging of a battery.

A Li-ion battery may be placed in a multi-channel battery cycler 202 to charge and discharge the battery. The charging and discharging of a battery can occur according to changes in two conditions:

Minimum SOC (e.g. voltage) cutoff (SOC ¹ , SOC ² , ..., SOC ^p )

Cycling current (I ¹ , I ² , ..., I ^q )

The voltage range can vary from 4.3v-3v or 4.4v to 3v. The number of cycles (n) may be determined based on maximum allowable degradation conditions. For example, if a cell cycles at its maximum allowable current (I ^max ) in the maximum SOC range (0-1), the cell will reach the end of its life capacity (eg, 80% of its new cell capacity) after 100 cycles. %) can be reached. Then, n=100 may be.

Since the effect of current is expected to be high at high discharge rates, for a good evaluation of the degradation function, the current range can be covered by 5 values (e.g. q~5). On the other hand, 10 values of minimum SOC (eg, p~10) should be considered.

After completing the process of charging and discharging for 100 cycles, the capacity obtained for a specific voltage range can be denoted by Cn. Since the SOC and current are directly connected to the voltage range, the obtained capacitance can be fed to the capacitance fade modeler 204.

The capacity degradation modeler 204 may calculate a capacity degradation rate of the battery using data collected from the multi-channel battery cycler 202 . The amount of charge (or charge) going into the battery may appear to decrease significantly after a number of cycles. Thus, the amount of charge (or charge) present in the battery after 100 cycles versus the amount of charge (or charge) present in the fresh battery can be defined as the ratio of Cn to C0.

For example, all of the above data sets can be collected offline for 3-4 months using a 25 channel battery cycler and fed into the capacity degradation modeler 204.

The capacity degradation modeler 204 may apply data collected from the multi-channel cyclo 204, that is, capacity degradation data to Equation 16. The capacity degradation modeler 204 may generate a capacity degradation model and transmit the generated capacity degradation model to the onboard controller 203 .

The on-board controller 203 may estimate a capacity degradation rate for a battery, for example, a Li-ion battery, using a capacity degradation model. In addition, the onboard controller 203 may predict the remaining life of the battery.

The predicted remaining life may be communicated to the battery-equipped device 208, such as a cell phone or electric vehicle. That is, the capacity degradation model generated offline can be helpful in predicting the remaining life of a battery according to random use of a mobile phone or an electric vehicle onboard. It becomes possible to provide feedback from a mobile phone or electric vehicle regarding battery usage which helps the controller unit provide better values.

3A and 3B show examples of graphical representations of the results of capacity degradation data of a rechargeable battery obtained using a capacity degradation model.

Deterioration data of the Li-NCM cell can be obtained using the following formula, Equation 17.

A fitting form representing the capacity degradation rate as a function of SOC obtained using the above formula is as shown in FIG. 3A. Similarly, the rate of capacity degradation is obtained as a function of SOC using the experimental setup (or device) of the embodiment, and is shown as a dot in FIG. 3A. In Fig. 3a, the fitted curve is slightly over-predicted at SOC = 0.4 and slightly under-predicted at SOC = 0.37 (slightly under-predicted).

Similarly, fitting and experimental results showing the rate of capacity degradation of a rechargeable battery as a function of current (I) using a capacity degradation model are shown in FIG. 3B. As shown in FIG. 3B , the fitting and experimental results obtained using the capacity fade model agree with each other, and the predicted results remain the same.

4A to 4E show validation results of capacity degradation rates of rechargeable batteries obtained for different voltage ranges.

을 갖는 구형(spherical)인 것으로 가정한다. 캐소드 물질(cathode material)의 최대 부피 용량(maximum volumetric capacity)은

로 표시될 수 있다. 용량이 저하되면, 이 양은 변하지 않는다.In one exemplary operation, it is assumed that the number of cathode particles in the cathode is N, and the cathode particles have a radius

It is assumed to be spherical with The maximum volumetric capacity of the cathode material is

can be displayed as When the capacity is lowered, this amount does not change.

From the definition of the total capacity (C), the relationship between the particle radius and the total capacity by conservation of lithium storage capacity can be derived as in Equation 18 (or can be derived ).

For the constant current condition, using finite approximation, Equation 19 can be obtained.

Capacity and particle radius can be related as in Equation 20.

The cathode particle size can be updated as shown in Equation 21 in a pseudo two-dimension (P2D) electrochemical model framework.

The model mentioned above can be verified against experimental data as follows. This method can be applied to Equation 22 mentioned below.

함수를 사용하여 수행될 수 있다. Li-NCM (NiMnCo)에서의 용량 손실은 캐소드 입자 분해(cathode particle dissolution)의 결과임이 알려져 있다. 현재 모델에 의해 예측된 용량 손실은 물리 기반 모델(physics based model)에서 구현된다.This current dependent function is considered unity, say 1, since there is no change in current. There is no way to formulate the current dependence function. Thus, cycling is smooth

This can be done using a function. It is known that capacity loss in Li-NCM (NiMnCo) is a result of cathode particle dissolution. The capacity loss predicted by the current model is implemented in a physics based model.

The capacity of the cell can be predicted under different operating conditions for multiple cycles and shown as shown in FIGS. 4A-4E. The validation results have high prediction accuracy, exceeding 90% for all cases. Also, as shown in FIG. 4E, the smooth function can capture a high capacity fade trend in the 4.7V-3.0V discharge condition.

A generalized capacity degradation model for any Li-ion battery can be established (or prepared) through this method. Since the time and effort for data collection is small, the model can be easily implemented into a battery management system in a form close to that of a capacity degradation model. The embodiment can be used in conjunction with other established models, for example the decomposition model due to SEI growth in a graphite anode. Further, embodiments may predict life expectancy for any usage pattern, including learned from use or supplied by the user.

Capacity degradation data collected onboard can be analyzed in the cloud for optimized performance, such as life extension and performance enhancement. Embodiments may provide long term benefit analysis through applications based on smart power management tools. In addition, the rate of capacity degradation can be easily detected by comparing specific usage profile inputs and corresponding outputs using an embodiment.

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 on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. 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.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

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

Claims (12)

collecting capacity degradation data of the battery, regarding state of charge (SOC) and current, for a predefined number of cycles by repeating charge-discharge cycles for the battery at different voltage ranges;
generating a capacity degradation model using the capacity degradation data; and
Estimating a capacity fade rate for the battery using the capacity fade model
including,
The capacity reduction model,
defined as the logarithmic difference in fractional capacity after cycling between different SOCs divided by the difference between the SOCs multiplied by the number of cycles;
The partial capacity is
A method for predicting the rate of capacity degradation of a battery, defined as the ratio of cell capacity to initial cell capacity at the end of cycling.
delete According to claim 1,
the battery,
Nickel-cadmium battery, nickel-metal hydride battery, lithium-ion battery, lithium sulfur battery, thin film battery, Capacity degradation rate of a battery selected from the group comprising at least one of a carbon foam-based lead acid battery, a potassium-ion battery, and a sodium-ion battery prediction method.
According to claim 1,
The method of predicting a capacity degradation rate of a battery in which the predefined number of cycles is determined based on maximum allowable degradation conditions.
According to claim 1,
The capacity degradation data is collected according to changes in conditions,
The condition includes a first condition in which deterioration is the same during charge and discharge cycles and a second condition in which deterioration is different during charge and discharge cycles in which the charging current is constant.
According to claim 1,
The capacity decrease rate is notified through an application of a user's mobile phone.
A multi-channel battery cycler that collects capacity degradation data of the battery in terms of state of charge (SOC) and current for a predefined number of cycles by repeating charge-discharge cycles on the battery at different voltage ranges. ;
a capacity degradation modeler generating a capacity degradation model using the capacity degradation data; and
A controller for estimating a capacity fade rate for the battery using the capacity fade model
including,
The capacity reduction model,
defined as the logarithmic difference in fractional capacity after cycling between different SOCs divided by the difference between the SOCs multiplied by the number of cycles;
The partial capacity is
A device defined as the ratio of cell capacity to initial cell capacity at the end of cycling.
delete According to claim 7,
the battery,
Nickel-cadmium battery, nickel-metal hydride battery, lithium-ion battery, lithium sulfur battery, thin film battery, A device selected from the group comprising at least one of a carbon foam-based lead acid battery, a potassium-ion battery, and a sodium-ion battery.
According to claim 7,
wherein the predefined number of cycles is determined based on maximum allowable degradation conditions.
According to claim 7,
The capacity degradation data is collected according to changes in conditions,
wherein the conditions include a first condition in which degradation is equal during charge and discharge cycles and a second condition in which degradation is different during charge and discharge cycles in which the charging current is constant.
According to claim 7,
The capacity reduction rate is notified through an application of a user's mobile phone.

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