Apparatus and method for predicting life of battery module and rack by using battery cell test data

Abstract

The present invention relates to an apparatus and a method for predicting the life of a battery module and a battery rack by reflecting an electrical / environmental difference between a battery cell and a battery module / rack with respect to charging / discharging test data at a battery cell level.　The apparatus for predicting the life of a battery of the present invention comprises: a cell life prediction unit that analyzes charge / discharge test data of a battery cell to estimate the life of a battery cell; a module coefficient calculation unit that calculates a module coefficient for a battery module; a module-level life prediction unit that reflects the module coefficient of the module coefficient calculation unit in a cell life prediction value of the cell life prediction unit to estimate the life at the battery module level; a rack coefficient calculation unit that calculates a rack coefficient for a battery rack; and a rack-level life prediction unit that reflects the rack coefficient of the rack coefficient calculation unit in a module-level life prediction value of the module-level life prediction unit to estimate the life at the battery rack level.

Description

Device and method for predicting the lifespan of battery modules and racks using battery cell test data

[0001] The present invention relates to a device and method for predicting battery life, and more particularly, to a device and method for predicting battery life based on a battery charge / discharge test.

[0002] The recent rapid increase in electricity demand has driven the expansion of renewable energy generation and the development of battery-based energy storage systems (ESS). ESS is a large-capacity power storage device that stores excess power in batteries and discharges it when power is in short supply. Using ESS can reduce investment costs for new power generation facilities and maximize energy production and utilization efficiency through integration with renewable energy sources like wind and solar energy.

[0003] ESS consists of multiple batteries, a power conditioning system (PCS), and other components. The PCS essentially performs bidirectional power control of DC and AC power between the power grid and the batteries, and also enhances the reliability of the power grid and ensures the rapid supply of stored energy during peak power demand.

[0004] In addition, battery devices generally use secondary batteries that have a discharge function that converts chemical energy into electrical energy and a charge function that stores chemical energy using an external power source.

[0005] These battery devices can be composed of battery cells, battery modules, or battery racks. Battery cells individually store and release energy, while battery modules are battery assemblies that are grouped together in a frame to protect them from external shocks, heat, and vibration. These battery modules consist of multiple cells connected in series and / or parallel, and are typically embedded within a mechanical structure.

[0006] Battery racks are designed to safely protect, cool, and manage power for multiple battery modules, and are primarily used in large-capacity battery systems. These battery racks manage power and communication connections between battery modules and perform the functions necessary to maintain the stability and efficiency of the battery system.

[0007] Battery devices experience a shortened lifespan as they are repeatedly charged and discharged, and research has been ongoing to predict this lifespan.

[0008] As an example, Korean Patent Publication No. 10-2537607 proposes a method for generating first sensing information representing the voltage and current of a battery while the battery is charged with a first constant current, generating second sensing information representing the voltage and current of the battery during a second period while the battery is discharged with a second constant current, and determining the degree of degradation of a battery based on the voltage value of a first charging characteristic point of a first differential capacity curve based on the first sensing information and the voltage value of a first discharging characteristic point of a second differential capacity curve.

[0009] However, most existing battery life estimation methods only consider conditions such as SOC (State of Charge) and C-rate during the battery cell testing process, and do not consider factors arising from differences with battery modules or battery racks.

[0010] Therefore, in the past, it was impossible to consider the causal relationship between the lifespan (capacity decline) at the battery cell level and the lifespan at the battery module / rack level, and thus the prediction results could not be extended to the battery module / rack level. In addition, there was a problem that it was difficult to accurately predict the battery lifespan because a margin was applied to the capacity at the battery cell level for the lifespan at the battery module / rack level.

[0011] The purpose of the present invention is to provide a battery life prediction device and method capable of predicting the life of a battery module and a battery rack from charge / discharge test data at the battery cell level in predicting battery deterioration (lifespan reduction).

[0012] In addition, another purpose of the present invention is to provide a battery life prediction device and method capable of accurately predicting battery life by complementing the limitations of predicting life at the battery cell level, which is a shortcoming of the existing method, through module coefficients and rack coefficients.

[0013] Another object of the present invention is to provide a battery life prediction device and method that can improve the stability and efficiency of ESS operation by enabling accurate life prediction of battery modules and battery racks.

[0014] A battery life prediction device according to one embodiment of the present invention may include a cell life prediction unit that analyzes charge / discharge test data of a battery cell to estimate the life of a battery cell, a module coefficient calculation unit that calculates a module coefficient for a battery module, a module-level life prediction unit that estimates the life at a battery module level by reflecting the module coefficient of the module coefficient calculation unit to the cell life prediction value of the cell life prediction unit, a rack coefficient calculation unit that calculates a rack coefficient for a battery rack, and a rack-level life prediction unit that estimates the life at a battery rack level by reflecting the rack coefficient of the rack coefficient calculation unit to the module-level life prediction value of the module-level life prediction unit.

[0015] Here, the module coefficient calculation unit can calculate the module coefficient based on factors causing differences in deterioration for each battery module.

[0016] At this time, the factors causing the difference in deterioration of each battery module may include at least one of cell-specific deviation, cell-specific deterioration degree difference due to conduction resistance difference, cell-specific heat dissipation difference, or SOC-Ah throughput ratio change.

[0017] Meanwhile, the module level life prediction unit can calculate the module level life prediction value by additionally reflecting the cell level prediction error value to the battery cell life prediction value estimated by the cell life prediction unit.

[0018] In addition, the rack coefficient calculation unit can derive the rack coefficient based on factors causing differences in deterioration for each battery rack.

[0019] Here, the factors causing the difference in deterioration by battery rack may include at least one of temperature difference, error due to BMS (Battery Management System), and difference in deterioration degree by module due to difference in conduction resistance.

[0020] At this time, the temperature difference may include at least one of a battery room reference temperature error, a vertical temperature difference between modules, and a horizontal temperature difference between racks.

[0021] Additionally, the error caused by the BMS (Battery Management System) may include at least one of an SOC error, a current measurement error, and a voltage measurement error.

[0022] In addition, the rack-level life prediction unit can calculate a rack-level life prediction value by additionally reflecting a module-level prediction error value to the module-level life prediction value estimated by the module-level life prediction unit.

[0023] Meanwhile, a battery life prediction method according to an embodiment of the present invention may include a cell life prediction step of analyzing charge / discharge test data of a battery cell to estimate the life of a battery cell; a module coefficient calculation step of calculating a module coefficient for a battery module; a module level life prediction step of reflecting the module coefficient of the module coefficient calculation step to the cell life prediction value of the cell life prediction step to estimate the life at the battery module level; a rack coefficient calculation step of calculating a rack coefficient for a battery rack; and a rack level life prediction step of reflecting the rack coefficient of the rack coefficient calculation step to the module level life prediction value of the module level life prediction step to estimate the life at the battery rack level.

[0024] Here, the module coefficient calculation step can calculate the module coefficient based on factors causing differences in deterioration for each battery module.

[0025] In addition, factors causing differences in deterioration of the battery modules may include at least one of cell-specific deviations, cell-specific deterioration differences due to differences in conduction resistance, cell-specific heat dissipation differences, or changes in the SOC-Ah throughput ratio.

[0026] In addition, the module level life prediction step can calculate the module level life prediction value by additionally reflecting the cell level prediction error value to the battery cell life prediction value estimated in the cell life prediction step.

[0027] Meanwhile, the above rack coefficient calculation step can derive the rack coefficient based on factors causing differences in deterioration by battery rack.

[0028] Here, the factors causing the difference in deterioration by battery rack may include at least one of temperature difference, error due to BMS (Battery Management System), and difference in deterioration degree by module due to difference in conduction resistance.

[0029] Additionally, the temperature difference may include at least one of a battery room reference temperature error, a vertical temperature difference between modules, and a horizontal temperature difference between racks.

[0030] Additionally, the error caused by the BMS (Battery Management System) may include at least one of an SOC error, a current measurement error, and a voltage measurement error.

[0031] Meanwhile, the rack-level life prediction step can calculate a rack-level life prediction value by additionally reflecting a module-level prediction error value to the module-level life prediction value estimated in the module-level life prediction step.

[0032] The battery life prediction device and method according to the present invention can predict the life of a battery module and a battery rack from charge / discharge test data at the battery cell level in predicting deterioration (reduction in lifespan) of a battery.

[0033] In addition, the battery life prediction device and method according to the present invention can accurately predict the battery life by complementing the limitations of the existing method in predicting the life at the battery cell level through the module coefficient and the rack coefficient.

[0034] In addition, the battery life prediction device and method according to the present invention can improve the stability and efficiency of ESS operation by enabling accurate life prediction for battery modules and battery racks.

[0035] FIG. 1 is a drawing showing a battery life prediction device according to one embodiment of the present invention.

[0036] Figure 2 is a drawing for explaining the operation of the module coefficient calculation unit of Figure 1.

[0037] Fig. 3 is a drawing for explaining the operation of the rack coefficient calculation unit of Fig. 1.

[0038] Figure 4 is a flowchart illustrating a battery life prediction method according to one embodiment of the present invention.

[0039] Hereinafter, specific embodiments for carrying out the present invention will be described with reference to the attached drawings.

[0040] When describing the present invention, terms such as "first" and "second" may be used to describe various components. However, the components may not be limited by these terms. The terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, the first component could be referred to as the "second component," and similarly, the second component could also be referred to as the "first component."

[0041] When it is said that a component is connected or connected to another component, it can be understood that it may be directly connected or connected to that other component, but there may also be other components in between.

[0042] The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the present invention. Singular expressions may include plural expressions unless the context clearly dictates otherwise.

[0043] In this specification, terms such as “include” or “have” are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, and can be understood as not excluding in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0044] Additionally, the shape and size of elements in the drawing may be exaggerated for clearer explanation.

[0045] Hereinafter, a battery life prediction device and method according to the present invention will be described in detail with reference to the attached drawings.

[0046]

[0047] The present invention proposes a method for predicting the lifespan of a battery module in which a plurality of battery cells are connected in series / parallel and a battery rack in which a plurality of battery modules are connected in series / parallel by reflecting electrical / environmental differences between battery cells and battery modules / racks in charge / discharge test data at the battery cell level in predicting battery deterioration (reduction in lifespan).

[0048] That is, the present invention predicts the life of a battery module / rack by adding a factor item of a battery module / rack to an existing life prediction method and applying a design value for each factor, thereby introducing a module factor and a rack factor that reflect the characteristics of the current battery module / rack into the life test results at the battery cell level, and extending the application to life prediction for a battery module / rack.

[0049] The present invention can complement the limitations of existing battery cell level life prediction methods, thereby enabling more realistic and accurate battery life prediction.

[0050] FIG. 1 is a drawing showing a battery life prediction device according to one embodiment of the present invention, and FIGS. 2 and 3 are detailed drawings for explaining FIG. 1 in detail.

[0051] Hereinafter, a battery life prediction device according to one embodiment of the present invention will be described with reference to FIGS. 1 to 3.

[0052] First, referring to FIG. 1, a battery life prediction device according to the present invention comprises a cell life prediction unit (100) that analyzes charge / discharge test data of a battery cell to estimate the life of a battery cell; a module coefficient calculation unit (200) that calculates a module coefficient for a battery module; a module-level life prediction unit (300) that estimates the life at the battery module level by reflecting the module coefficient of the module coefficient calculation unit (200) to the cell life prediction value of the cell life prediction unit (100); a rack coefficient calculation unit (400) that calculates a rack coefficient for a battery rack; and a rack-level life prediction unit (500) that estimates the life at the battery rack level by reflecting the rack coefficient of the rack coefficient calculation unit (400) to the module-level life prediction value of the module-level life prediction unit (300).

[0053] Here, the cell life prediction unit (100) can use various methods to estimate the life of the battery cell based on the charge / discharge test data of the battery cell.

[0054] For example, battery deterioration can be expressed in the form of the Arrhenius equation, and based on this, by reflecting the SOC (State-of-Charge), C-rate, temperature, and charge / discharge amount according to the battery charge / discharge conditions, battery deterioration can be expressed as in the following mathematical equation 1.

[0055] [Mathematical Formula 1]

[0056]

[0057] Here, Q cyc : Capacity at the sampling point, α, β: optimization parameters, SOC: State-of-charge, E a: Activation Energy, Crate : C-rate, R gas : Gas constant, T k : Temperature, Ah : Ampere-hour throughput, z : Power law exponent

[0058]

[0059] Additionally, the deterioration of the battery can be predicted based on a semi-empirical aging model, which is a model that can predict the battery life based on some charge / discharge data.

[0060] If the above battery degradation model is generalized, it can be expressed as shown in the following mathematical expression 2.

[0061] [Equation 2]

[0062]

[0063] Here, Q loss : Battery cell capacity degradation, SOC : State-of-charge, Crate : C-rate, T k : Temperature, Ah: Ampere-hour throughput

[0064]

[0065] Meanwhile, the battery deterioration model used in the cell life prediction unit (100) is a life prediction for a battery cell, and its accuracy is low when extended to life prediction at the battery module and battery rack levels.

[0066] Therefore, in the present invention, by applying module coefficients and rack coefficients that reflect the characteristics of the current battery module / rack to the cell life prediction value estimated by the cell life prediction unit (100), the life prediction is extended to the life prediction of the battery module / rack, thereby improving the life prediction accuracy at the battery module / rack level.

[0067] The module coefficient calculation unit (200) derives the module coefficient by reflecting the characteristics of the current battery module. At this time, Fig. 2 shows the factor items for reflecting the characteristics of the battery module.

[0068] That is, Fig. 2 illustrates factors causing differences in deterioration by battery module, and the module coefficient calculation unit (200) reflects the factors shown in Fig. 2 to calculate the module coefficient (K M ) is derived.

[0069] In the present invention, the module coefficient (K) for the current battery module is determined based on factors causing differences in deterioration of each battery module, such as cell deviation, differences in deterioration of each cell due to differences in conduction resistance, differences in heat dissipation of each cell, and changes in SOC-Ah throughput ratio. M ) is derived.

[0070] The module level life prediction unit (300) calculates the module coefficient (K) calculated from the module coefficient calculation unit (200) to the cell life prediction value from the cell life prediction unit (100). M ) is additionally reflected to estimate the lifespan at the battery module level.

[0071] The predicted lifespan value at the battery module level calculated by the module level lifespan prediction unit (300) is as shown in the following mathematical expression 3.

[0072] [Equation 3]

[0073]

[0074] Here, Q loss,M : Battery module capacity degradation, SOC : State-of-charge, Crate : C-rate, T k : Temperature, Ah : Ampere-hour throughput, K M : Module coefficient

[0075]

[0076] According to an implementation example, the module level life prediction unit (300) can additionally reflect a cell level prediction error value in the battery cell life prediction value estimated by the cell life prediction unit (100) to produce a module level life prediction value.

[0077] Meanwhile, the rack coefficient calculation unit (400) derives the rack coefficient by reflecting the characteristics of the current battery rack. At this time, Fig. 3 shows factor items for reflecting the characteristics of the battery rack.

[0078] That is, Fig. 3 illustrates factors causing differences in deterioration by battery rack, and the rack coefficient calculation unit (400) reflects the factors shown in Fig. 3 to calculate the rack coefficient (K R ) is derived.

[0079] In the present invention, the rack coefficient (K) for the current battery rack is determined based on factors that cause differences in deterioration by battery rack, such as temperature differences, errors due to BMS (Battery Management System), and differences in deterioration by module due to differences in conduction resistance. R ) is derived.

[0080] As shown in Fig. 3, temperature differences can reflect battery room reference temperature errors, vertical temperature differences between modules, and horizontal temperature differences between racks. Furthermore, errors due to the BMS (Battery Management System) can reflect SOC errors, current measurement errors, and voltage measurement errors.

[0081] Next, the rack level life prediction unit (500) calculates the rack coefficient (K) calculated from the rack coefficient calculation unit (400) to the module level life prediction value from the module level life prediction unit (300). R) is additionally reflected to estimate the lifespan at the battery rack level.

[0082] The predicted lifespan value at the battery rack level calculated by the rack level lifespan prediction unit (500) is as shown in the following mathematical expression 4.

[0083] [Equation 4]

[0084]

[0085] Here, Q loss,R : Battery Rack Capacity Decrease, SOC : State-of-charge, Crate : C-rate, T k : Temperature, Ah : Ampere-hour throughput, K M : Modulus coefficient, K R : Rack coefficient

[0086]

[0087] According to an implementation example, the rack-level life prediction unit (500) can calculate a rack-level life prediction value by additionally reflecting a module-level prediction error value to the module-level life prediction value estimated by the module-level life prediction unit (300).

[0088] According to the battery life prediction device of the present invention, the life of a battery module in which a plurality of battery cells are connected in series / parallel and a battery rack in which a plurality of battery modules are connected in series / parallel can be predicted from charge / discharge test data at the battery cell level, and the limitations of the existing life prediction at the battery cell level can be supplemented, enabling more realistic and accurate battery life prediction.

[0089]

[0090] Next, FIG. 4 is a flowchart illustrating a battery life prediction method according to one embodiment of the present invention.

[0091] As can be seen in FIG. 4, the battery life prediction method according to the present invention comprises a cell life prediction step (S100) for estimating the life of a battery cell by analyzing charge / discharge test data of the battery cell; a module coefficient calculation step (S200) for calculating a module coefficient for a battery module; a module level life prediction step (S300) for estimating the life at the battery module level by reflecting the module coefficient of the module coefficient calculation step (S200) to the cell life prediction value of the cell life prediction step (S100); a rack coefficient calculation step (S400) for calculating the rack coefficient for a battery rack; and a rack level life prediction step (S500) for estimating the life at the battery rack level by reflecting the rack coefficient of the rack coefficient calculation step (S400) to the module level life prediction value of the module level life prediction step (S300).

[0092] Here, in the cell life prediction step (S100), various methods can be used to estimate the life of the battery cell based on the charge / discharge test data of the battery cell.

[0093] For example, battery degradation can be expressed in the form of the Arrhenius equation, and based on this, if the state-of-charge (SOC), C-rate, temperature, and charge / discharge amount according to the battery charge / discharge conditions are reflected, battery degradation can be expressed as in the mathematical equation 1 described above. Since this has been explained previously, a detailed explanation is omitted.

[0094] Additionally, the deterioration of the battery can be predicted based on a semi-empirical aging model, which is a model that can predict the battery life based on some charge / discharge data.

[0095] If the above battery degradation model is generalized and expressed as the above mathematical equation 2, a detailed explanation will be omitted as it has been explained previously.

[0096]

[0097] Meanwhile, the battery degradation model used in the cell life prediction step (S100) is a life prediction for a battery cell, and its accuracy is low when extended to life prediction at the battery module and battery rack levels.

[0098] Therefore, in the present invention, by applying module coefficients and rack coefficients that reflect the characteristics of the current battery module / rack to the battery cell life prediction value estimated in the cell life prediction step (S100), the life prediction is extended to the life prediction of the battery module / rack, thereby improving the life prediction accuracy at the battery module / rack level.

[0099] In the module coefficient calculation step (S200), the module coefficient is derived by reflecting the characteristics of the current battery module. At this time, the factor items for reflecting the characteristics of the battery module are shown in the above-described Figure 2.

[0100] That is, Fig. 2 illustrates factors causing differences in deterioration by battery module, and in the module coefficient calculation step (S200), the factors shown in Fig. 2 are reflected to calculate the module coefficient (K). M ) is derived.

[0101] In the present invention, the module coefficient (K) for the current battery module is determined based on factors causing differences in deterioration of each battery module, such as cell deviation, differences in deterioration of each cell due to differences in conduction resistance, differences in heat dissipation of each cell, and changes in SOC-Ah throughput ratio. M ) is derived.

[0102] In the module level life prediction step (S300), the module coefficient (K) calculated from the module coefficient calculation step (S200) is added to the cell life prediction value from the cell life prediction step (S100). M ) is additionally reflected to estimate the lifespan at the battery module level.

[0103] The predicted lifespan value at the battery module level calculated in the module level lifespan prediction step (S300) is as shown in the mathematical expression 3 described above. Since this has been explained previously, a detailed explanation is omitted.

[0104] According to an implementation example, in the module level life prediction step (S300), a cell level prediction error value may be additionally reflected in the battery cell life prediction value estimated in the cell life prediction step (S100) to produce a module level life prediction value.

[0105] Next, the rack coefficient calculation step (S400) derives the rack coefficient by reflecting the characteristics of the current battery rack.

[0106] The above-described Figure 3 illustrates factors causing differences in deterioration by battery rack, and the rack coefficient calculation unit (310) reflects the factors shown in Figure 3 to calculate the rack coefficient (K R ) is derived.

[0107] In the present invention, the rack coefficient (K) for the current battery rack is determined based on factors that cause differences in deterioration by battery rack, such as temperature differences, errors due to BMS (Battery Management System), and differences in deterioration by module due to differences in conduction resistance. R ) is derived.

[0108] As shown in Fig. 3, temperature differences can reflect battery room reference temperature errors, vertical temperature differences between modules, and horizontal temperature differences between racks. Furthermore, errors due to the BMS (Battery Management System) can reflect SOC errors, current measurement errors, and voltage measurement errors.

[0109] In the rack level life prediction step (S500), the rack coefficient (K) calculated from the rack coefficient calculation step (S400) is added to the module level life prediction value from the module level life prediction step (S300). R ) is additionally reflected to estimate the lifespan at the battery rack level.

[0110] The predicted lifespan value at the battery rack level calculated in the rack-level lifespan prediction step (S500) is as shown in the mathematical expression 4 described above. Since this has been explained previously, a detailed explanation is omitted.

[0111] According to an implementation example, in the rack-level life prediction step (S500), a module-level prediction error value may be additionally reflected in the module-level life prediction value estimated in the module-level life prediction step (S300) to produce a rack-level life prediction value.

[0112] According to the battery life prediction method of the present invention, the life of a battery module in which a plurality of battery cells are connected in series / parallel and a battery rack in which a plurality of battery modules are connected in series / parallel can be predicted from charge / discharge test data at the battery cell level, and the limitations of the existing life prediction at the battery cell level can be supplemented, enabling more realistic and accurate battery life prediction.

[0113]

[0114] According to the battery life prediction device and method of the present invention as described above, the lifespan of a battery module and battery rack can be predicted from battery cell-level charge / discharge test data. Furthermore, by complementing the limitations of existing methods in predicting lifespan at the battery cell level through module and rack coefficients, accurate battery lifespan prediction is possible. Furthermore, by enabling accurate lifespan prediction for battery modules and battery racks, the stability and efficiency of ESS operation can be improved.

[0115]

[0116] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0117] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0118] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present invention based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0119] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0120] The present invention relates to a battery life prediction device and method, and is applicable to the battery field.

Claims

1. A cell life prediction unit that estimates the life of a battery cell by analyzing charge / discharge test data of the battery cell; A module coefficient calculation unit that calculates a module coefficient for a battery module; A module level life prediction unit that estimates the life at the battery module level by reflecting the module coefficient of the module coefficient calculation unit to the cell life prediction value of the cell life prediction unit; A rack coefficient calculation unit for calculating a rack coefficient for a battery rack; and A battery life prediction device including a rack-level life prediction unit that estimates the life at the battery rack level by reflecting the rack coefficient of the rack coefficient calculation unit to the module-level life prediction value of the module-level life prediction unit.

2. In paragraph 1, The above module coefficient calculation section is, A battery life prediction device characterized by calculating a module coefficient based on factors causing differences in deterioration of each battery module.

3. In paragraph 2, The factors causing the difference in deterioration of each battery module are: A battery life prediction device characterized by including at least one of cell-specific deviation, cell-specific degradation degree difference due to conduction resistance difference, cell-specific heat dissipation difference, or SOC-Ah throughput ratio change.

4. In paragraph 1, The above module level life prediction unit is, A battery life prediction device characterized in that the module level life prediction value is calculated by additionally reflecting a cell level prediction error value to the battery cell life prediction value estimated by the cell life prediction unit.

5. In paragraph 1, The above rack coefficient calculation section, A battery life prediction device characterized by deriving a rack coefficient based on factors causing differences in deterioration by battery rack.

6. In paragraph 5, The factors causing the difference in deterioration of the above battery racks are: A battery life prediction device characterized by including at least one of a temperature difference, an error due to a BMS (Battery Management System), and a difference in the degree of deterioration of each module due to a difference in conduction resistance.

7. In paragraph 6, As for the above temperature difference, A battery life prediction device characterized by including at least one of a battery room reference temperature error, a vertical temperature difference between modules, and a horizontal temperature difference between racks.

8. In paragraph 6, As for the error caused by the above BMS (Battery Management System), A battery life prediction device characterized by including at least one of a SOC error, a current measurement error, and a voltage measurement error.

9. In paragraph 1, The above rack level life prediction unit is, A battery life prediction device characterized in that a rack-level life prediction value is calculated by additionally reflecting a module-level prediction error value to the module-level life prediction value estimated by the module-level life prediction unit.

10. Cell life prediction step that estimates the life of a battery cell by analyzing charge / discharge test data of the battery cell; Module coefficient calculation step for calculating module coefficient for battery module; A module level life prediction step for estimating the life at the battery module level by reflecting the module coefficient of the module coefficient calculation step to the cell life prediction value of the cell life prediction step; A rack coefficient calculation step for calculating a rack coefficient for a battery rack; and A battery life prediction method comprising a rack-level life prediction step for estimating the life at the battery rack level by reflecting the rack coefficient of the rack coefficient calculation step to the module-level life prediction value of the module-level life prediction step.

11. In paragraph 10, The above module coefficient calculation step is: A battery life prediction method characterized by calculating a module coefficient based on factors causing differences in deterioration of each battery module.

12. In paragraph 11, The factors causing the difference in deterioration of each battery module are: A battery life prediction method characterized by including at least one of cell-specific deviation, cell-specific degradation degree difference due to conduction resistance difference, cell-specific heat dissipation difference, or SOC-Ah throughput ratio change.

13. In paragraph 10, The above module level life prediction step is: A battery life prediction method characterized in that the module level life prediction value is calculated by additionally reflecting a cell level prediction error value to the battery cell life prediction value estimated in the cell life prediction step.

14. In paragraph 10, The above rack coefficient calculation step is: A battery life prediction method characterized by deriving a rack coefficient based on factors causing differences in deterioration by battery rack.

15. In paragraph 14, The factors causing the difference in deterioration of the above battery racks are: A battery life prediction method characterized by including at least one of a temperature difference, an error due to a BMS (Battery Management System), and a difference in the degree of deterioration of each module due to a difference in conduction resistance.

16. In paragraph 15, As for the above temperature difference, A battery life prediction method characterized by including at least one of a battery room reference temperature error, a vertical temperature difference between modules, and a horizontal temperature difference between racks.

17. In paragraph 15, As for the error caused by the above BMS (Battery Management System), A battery life prediction method characterized by including at least one of SOC error, current measurement error, and voltage measurement error.

18. In paragraph 10, The above rack level life prediction step is: A battery life prediction method characterized in that a rack-level life prediction value is calculated by additionally reflecting a module-level prediction error value to the module-level life prediction value estimated in the above module-level life prediction step.

Images