Battery diagnosis device and method

Abstract

A battery diagnosis device according to one embodiment of the present invention may comprise: at least one processor; and memory for storing at least one command that is executed through the at least one processor. The at least one command may comprise: a command for collecting battery capacities calculated during charge / discharge cycles of a battery, the battery capacities being calculated for each of the charge / discharge cycles; a command for defining a capacity threshold range for the current charge / discharge cycle on the basis of the battery capacities for a plurality of predefined cycles; and a command for determining whether the battery is abnormal on the basis of whether the battery capacity for the current charge / discharge cycle is within the capacity threshold range.

Description

Battery diagnostic device and method

[0001] This application claims the benefit of Korean Patent Application No. 10-2023-0159979 filed with the Korean Intellectual Property Office on November 20, 2023, the entire disclosure of which is incorporated herein by reference.

[0002] The present invention relates to a battery diagnostic device and method, and more particularly, to a battery diagnostic device and method that diagnoses whether a battery is abnormal based on battery capacity per cycle collected during a charge / discharge cycle of the battery.

[0003] Secondary batteries are batteries that can be reused by charging even after discharge, and can be used as an energy source for small devices such as mobile phones, tablet PCs, and vacuum cleaners, and are also used as an energy source for medium and large devices such as automobiles and ESS (Energy Storage Systems) for smart grids.

[0004] Secondary batteries are applied to systems in the form of assemblies, such as battery modules, in which multiple battery cells are connected in series and parallel, or battery racks, in which battery modules are connected in series and parallel, depending on the system requirements. For medium- to large-sized devices, high-capacity battery systems, in which multiple battery modules are connected in parallel, may be applied to meet the device's capacity requirements.

[0005] During the battery cell manufacturing process, various tests may be conducted to assess safety, performance, defects, and more. Specifically, stability tests such as overcurrent tests, high-temperature storage tests, short-circuit tests, and penetration tests may be performed to assess safety against defects that may occur during battery operation.

[0006] Additionally, long-term charge-discharge tests can be performed to evaluate the performance of batteries over long periods of use. For example, a charge-discharge test device can collect discharge capacity data for each charge-discharge cycle, calculate the slope of the discharge capacity, and then, based on the calculated slope, detect battery cells that are experiencing or at risk of performance degradation.

[0007] However, in the case of these charge / discharge test methods, since abnormal batteries are detected based only on the discharge capacity slope, the diagnostic accuracy is low and the misdiagnosis rate is high.

[0008] Therefore, to solve these problems, appropriate diagnostic technology is needed that can detect battery performance abnormalities early and more accurately.

[0009] The purpose of the present invention to solve the above problems is to provide a battery diagnostic device that diagnoses whether a battery is abnormal based on the battery capacity per cycle collected during the charge / discharge cycle of the battery.

[0010] Another object of the present invention to solve the above problems is to provide a battery diagnosis method using such a battery diagnosis device.

[0011] A battery diagnostic device according to one embodiment of the present invention for achieving the above purpose may include at least one processor; and a memory storing at least one command executed through the at least one processor.

[0012] Here, the at least one command may include a command for collecting battery capacity per cycle, which is calculated during a charge / discharge cycle of the battery; a command for defining a capacity threshold range for a current charge / discharge cycle based on battery capacities for a predetermined number of cycles; and a command for determining whether the battery is abnormal based on whether the capacity for the current charge / discharge cycle of the battery is within the capacity threshold range.

[0013] The command for collecting the battery capacity per cycle may include a command for collecting the discharge capacity of the battery per cycle.

[0014] The command defining the above capacity threshold range may include a command defining the capacity threshold range for the current charge / discharge cycle based on the battery capacity for the most recent N charge / discharge cycles (where N is a natural number greater than or equal to 3).

[0015] The command defining the above capacity threshold range may include a command calculating a capacity reduction rate based on battery capacities of previous cycles; a command calculating a predicted minimum value and a predicted maximum value for the capacity of the current cycle based on the capacity reduction rate; and a command defining the capacity threshold range based on the predicted minimum value and the predicted maximum value.

[0016] The command for calculating the capacity reduction rate may include a command for calculating the capacity reduction rate based on the difference value between the largest battery capacity and the smallest battery capacity among the battery capacities of the most recent N cycles (N is a natural number greater than or equal to 3).

[0017] The command for calculating the predicted minimum value and predicted maximum value for the capacity of the current cycle may include a command for predicting the minimum value of the capacity reduction amount based on the capacity of the most recently performed M cycles (M is a natural number greater than or equal to 1 and less than N) among the most recent N cycles (N is a natural number greater than or equal to 3) and the capacity reduction rate; a command for predicting the maximum value of the capacity reduction amount based on the capacity of the first performed L cycles (L is a natural number greater than or equal to 1 and less than N) among the N cycles and the capacity reduction rate; and a command for calculating the predicted minimum value and predicted maximum value based on the minimum and maximum values ​​of the capacity reduction amount.

[0018] The command for calculating the predicted minimum value and predicted maximum value for the capacity of the current cycle may include a command for calculating the predicted minimum value by subtracting the maximum value of the capacity reduction amount from the average value or median value for the capacity of the M cycles; and a command for calculating the predicted maximum value by subtracting the minimum value of the capacity reduction amount from the average value or median value for the capacity of the L cycles.

[0019] The command for determining whether the battery is abnormal may include a command for classifying the battery as an abnormal battery if the capacity for the current charge / discharge cycle is outside the capacity threshold range.

[0020] The command defining the above capacity threshold range may include a command for sequentially updating the capacity threshold range as charge / discharge cycles for the battery sequentially proceed. Here, the command for determining whether the battery is abnormal may include a command for determining whether the battery is abnormal based on the sequentially updated capacity threshold range.

[0021]

[0022] A battery diagnosis method according to one embodiment of the present invention for achieving the above other objects may include a step of collecting battery capacities for each cycle calculated during a charge / discharge cycle of a battery; a step of defining a capacity threshold range for a current charge / discharge cycle based on battery capacities for a plurality of predefined cycles; and a step of determining whether the battery is abnormal based on whether the capacity for the current charge / discharge cycle of the battery is within the capacity threshold range.

[0023] The step of collecting the battery capacity per cycle may include a step of collecting the discharge capacity of the battery per cycle.

[0024] The step of defining the above capacity threshold range may include a step of defining the capacity threshold range for the current charge / discharge cycle based on the battery capacity for the most recent N charge / discharge cycles (N is a natural number greater than or equal to 3).

[0025] The step of defining the capacity critical range may include: calculating a capacity reduction rate based on battery capacities of previous cycles; calculating a predicted minimum value and a predicted maximum value for the capacity of the current cycle based on the capacity reduction rate; and defining the capacity critical range based on the predicted minimum value and the predicted maximum value.

[0026] The step of calculating the capacity reduction rate may include a step of calculating the capacity reduction rate based on the difference value between the largest battery capacity and the smallest battery capacity among the battery capacities of the most recent N cycles (N is a natural number greater than or equal to 3).

[0027] The step of calculating the predicted minimum value and predicted maximum value for the capacity of the current cycle may include the step of predicting the minimum value of the capacity reduction amount based on the capacity of M cycles (M is a natural number greater than or equal to 1 and less than N) that have recently been performed among N cycles (N is a natural number greater than or equal to 3) and the capacity reduction rate; the step of predicting the maximum value of the capacity reduction amount based on the capacity of L cycles (L is a natural number greater than or equal to 1 and less than N) that have previously been performed among N cycles and the capacity reduction rate; and the step of calculating the predicted minimum value and predicted maximum value based on the minimum and maximum values ​​of the capacity reduction amount.

[0028] The step of calculating the predicted minimum value and predicted maximum value for the capacity of the current cycle may include the step of calculating the predicted minimum value by subtracting the maximum value of the capacity reduction amount from the average value or median value for the capacity of the M cycles; and the step of calculating the predicted maximum value by subtracting the minimum value of the capacity reduction amount from the average value or median value for the capacity of the L cycles.

[0029] The step of determining whether the battery is abnormal may include a step of classifying the battery as an abnormal battery if the capacity for the current charge / discharge cycle is outside the capacity threshold range.

[0030] The step of defining the capacity threshold range may include a step of sequentially updating the capacity threshold range as charge and discharge cycles for the battery are sequentially performed. Here, the step of determining whether the battery is abnormal may include a step of determining whether the battery is abnormal based on the sequentially updated capacity threshold range.

[0031] According to the above-described embodiment of the present invention, after defining a capacity threshold range based on the battery capacity for each of a plurality of charge / discharge cycles, by diagnosing whether the battery is abnormal based on whether the current battery capacity is included in the capacity threshold range, it is possible to detect and more accurately determine performance abnormalities of the battery at an early stage.

[0032] Figure 1 is a flowchart of the operation of a general battery diagnosis method.

[0033] FIG. 2 is a block diagram showing a battery diagnosis system according to an embodiment of the present invention.

[0034] Figure 3 is an operation flowchart of a battery diagnosis method according to an embodiment of the present invention.

[0035] Figure 4 is a reference diagram for explaining a battery diagnosis method according to an embodiment of the present invention.

[0036] FIG. 5 is an operational flowchart of a method for defining a capacity threshold range according to an embodiment of the present invention.

[0037] FIG. 6 is a reference diagram for explaining a method for defining a capacity threshold range according to an embodiment of the present invention.

[0038] Figure 7 is a block diagram of a battery diagnostic device according to an embodiment of the present invention.

[0039] 10: Battery

[0040] 100: Battery assembly

[0041] 200, 700: Battery diagnostic device

[0042] The present invention is susceptible to various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, but rather to encompass all modifications, equivalents, and alternatives falling within the spirit and technical scope of the present invention. Throughout the description of each drawing, similar reference numerals have been used to designate similar components.

[0043] Terms such as "first," "second," "A," and "B" may be used to describe various components, but these components should not be limited by these terms. These 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." The term "and / or" includes any combination of multiple related items listed or any one of multiple related items listed.

[0044] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0045] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0046] 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 this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0047]

[0048] Some terms used in this specification are defined as follows:

[0049] SOC (State of Charge) is the current charged state of the battery expressed as a percentage [%], and SOH (State of Health) is the current deterioration state of the battery expressed as a percentage [%].

[0050] A battery cell is the smallest unit that stores electricity, and a battery module is a collection of multiple battery cells that are electrically connected.

[0051] A battery rack refers to a single-structure system that electrically connects module units designated by the battery manufacturer and can be monitored and controlled via a BMS. It can be configured to include multiple battery modules and a single BPU or protection device. Depending on the device or system in which the battery is used, the battery module may also be referred to as a battery pack.

[0052] A battery bank can refer to a large-scale battery rack system comprised of multiple battery racks connected in parallel. A battery bank-level BMS can monitor and control the rack BMS (RBMS) at the battery rack level.

[0053] A battery assembly is a collection of multiple electrically connected battery cells that function as a power source when applied to a specific system or device. Here, the battery assembly may refer to a battery module, battery pack, battery rack, or battery bank, but the scope of the present invention is not limited to these entities.

[0054]

[0055] Figure 1 is a flowchart of the operation of a general battery diagnosis method.

[0056] A typical battery diagnosis method can be performed by a battery diagnosis system that performs a long-term charge / discharge test on the battery to diagnose whether the battery is abnormal.

[0057] A charging / discharging device included in a battery diagnosis system performs a charging / discharging cycle of the battery (S110).

[0058] The battery diagnostic device included in the battery diagnostic system collects the discharge capacity per cycle, which is calculated during the charge / discharge cycle of the battery (S120).

[0059] Thereafter, the battery diagnostic device calculates the slope of the discharge capacity based on the collected discharge capacity per cycle (S130). Here, the slope of the discharge capacity can be calculated based on the difference value between the discharge capacity in the first cycle and the discharge capacity in the second cycle.

[0060] The battery diagnostic device can use the calculated discharge capacity slope to determine whether the battery is performing abnormally (S140). For example, if the discharge capacity in a specific cycle is below a preset threshold capacity and the discharge capacity slope is below the preset threshold slope, the battery diagnostic device can classify the battery as a battery with abnormal performance.

[0061] In the case of these general battery diagnosis methods, abnormal batteries are detected based only on the absolute value of discharge capacity and the slope of discharge capacity, so the diagnosis accuracy is low and the misdiagnosis rate is high.

[0062] The present invention is a technology devised to solve such problems, and various embodiments of the present invention will be described in detail below with reference to the attached drawings.

[0063]

[0064] FIG. 2 is a block diagram showing a battery diagnosis system according to an embodiment of the present invention.

[0065] Referring to FIG. 2, the battery diagnosis system may include a battery assembly (100) comprising a plurality of batteries (10) (BAT #1 to BAT #N) and a battery diagnosis device (200).

[0066] The battery diagnostic system according to the present invention can be implemented within a long-term charge / discharge test system, but the scope of the present invention is not limited to these entities. That is, the battery system according to the present invention can be applied to a specific device and operate to detect an abnormal battery by performing the battery diagnostic method described below.

[0067] A plurality of batteries (10) may be electrically connected and included in a battery assembly (100).

[0068] The battery (10) according to the present invention refers to a battery module or a battery pack, but the scope of the present invention is not limited thereto. That is, the battery system according to the present invention can operate to detect an object in which a performance abnormality has occurred by performing the battery diagnostic method described below on a battery cell, battery rack, or battery pack.

[0069] The battery diagnostic device (200) can be linked with a status measurement device that senses or calculates status information of the battery assembly (100).

[0070] The state measurement device can collect state data, such as battery temperature, current, and voltage, during the battery's charge / discharge cycle. Furthermore, the state measurement device can calculate battery capacity for each cycle using a predefined battery capacity calculation algorithm during the battery's charge / discharge cycle. Here, the battery capacity can correspond to either charge capacity or discharge capacity.

[0071] The status measurement device can transmit battery status data and battery capacity per cycle collected during the battery charge / discharge cycle to the battery diagnosis device (200). Meanwhile, the battery diagnosis device can be configured to directly calculate battery capacity based on the status data transmitted from the status measurement device.

[0072] The battery diagnostic device (200) can determine whether a battery has a performance abnormality based on the battery capacity per cycle. Specifically, the battery diagnostic device (200) can determine whether a battery has a performance abnormality based on whether the battery capacity per charge / discharge cycle falls within a cycle-specific critical capacity threshold range. Here, the cycle-specific critical capacity range can be defined based on the battery capacity for a predetermined number of cycles and can be updated each time a charge / discharge cycle is performed.

[0073] The battery diagnostic device (200) can determine whether a battery is abnormal for each charge / discharge cycle based on a sequentially updated cycle-by-cycle capacity threshold range. Here, if the battery capacity falls outside the capacity threshold range for the corresponding cycle, the battery diagnostic device (200) can classify the battery as an abnormal battery.

[0074]

[0075] Figure 3 is an operation flowchart of a battery diagnosis method according to an embodiment of the present invention.

[0076] A battery diagnosis method according to an embodiment of the present invention can be performed by a battery diagnosis device included in a battery diagnosis system.

[0077] The battery diagnostic device can collect cycle-by-cycle battery capacity calculated during the battery's charge / discharge cycle (S310). Here, the battery diagnostic device can receive cycle-by-cycle battery capacity from a status measurement device that collects battery status information. Alternatively, the battery diagnostic device can calculate cycle-by-cycle battery capacity based on cycle-by-cycle status data transmitted from the status measurement device.

[0078] In an embodiment, the battery diagnostic device can collect discharge capacity per cycle. That is, the battery diagnostic device can determine whether the battery is performing abnormally based on the discharge capacity per cycle.

[0079] The battery diagnosis device can define a capacity threshold range for a current charge / discharge cycle based on the battery capacity for a plurality of predefined cycles (S320). Here, the battery diagnosis device can define the capacity threshold range for the current charge / discharge cycle based on the battery capacity for the most recent N charge / discharge cycles (N is a natural number greater than or equal to 3). For example, the capacity threshold range for the current charge / discharge cycle (T-th cycle) can be defined based on the discharge capacity of each of the most recent five cycles (T-5th cycle to T-1th cycle). Meanwhile, N can be predefined as an appropriate number in consideration of diagnosis accuracy.

[0080] The battery diagnostic device can calculate a predicted minimum value and a predicted maximum value for the current cycle's capacity based on the battery capacity of past cycles, and define a capacity threshold range for the current charge / discharge cycle based on the calculated predicted minimum value and predicted maximum value. For example, if the predicted minimum value is calculated as C1 [Ah] and the predicted maximum value is calculated as C2 [Ah], the capacity threshold range can be defined as [C1 or more and C2 or less].

[0081] The battery diagnostic device can determine whether the battery is abnormal based on whether the battery capacity of the current charge / discharge cycle (T-th cycle) is within the capacity threshold range defined in S320 (S330).

[0082] If the battery capacity is outside the capacity threshold range for the cycle (N of S330), the battery diagnostic device can classify the battery as an abnormal battery.

[0083] If the battery capacity is within the capacity threshold range for the cycle (Y of S330), the battery diagnostic device can perform a diagnosis for the next charge / discharge cycle. That is, the battery diagnostic device can update the capacity threshold range for the T+1 cycle and compare the updated capacity threshold range with the battery capacity of the T+1 cycle to determine whether the battery is abnormal.

[0084]

[0085] FIG. 4 is a reference diagram illustrating a battery diagnosis method according to an embodiment of the present invention. Hereinafter, with reference to FIG. 4, an example of a battery diagnosis method using a battery diagnosis device will be described.

[0086] The battery diagnostic device can sequentially collect discharge capacity for each cycle as the battery's charge and discharge cycles are sequentially performed.

[0087] The battery diagnostic device can define a capacity threshold range for the current charge / discharge cycle based on the battery capacity for the most recent N cycles. For example, the battery diagnostic device can define a capacity threshold range for the 6th cycle based on the discharge capacity for each of the 1st to 5th cycles, as illustrated in Fig. 4(A). Here, the battery diagnostic device can calculate a predicted minimum value and a predicted maximum value for the capacity of the 6th cycle based on the discharge capacity for each of the 1st to 5th cycles, and define a capacity threshold range for the 6th cycle based on the calculated predicted minimum value and predicted maximum value. For example, if the predicted minimum value is calculated as C1 [Ah] and the predicted maximum value is calculated as C2 [Ah], the capacity threshold range can be defined as [C1 or more and C2 or less].

[0088] The battery diagnostic device can determine whether a battery is abnormal based on whether the battery capacity of the sixth cycle falls within the capacity threshold range defined for the sixth cycle. If the battery capacity of the sixth cycle falls outside the capacity threshold range for the sixth cycle, the battery may be classified as abnormal.

[0089] If the capacity threshold range for the 6th cycle is within the range, the battery diagnostic device can perform a diagnostic process for the 7th cycle.

[0090] That is, the battery diagnostic device can update the capacity threshold range for the 7th cycle based on the discharge capacity for each of the 2nd to 6th cycles, as illustrated in Fig. 4(B). Here, the battery diagnostic device can calculate a predicted minimum value and a predicted maximum value for the capacity of the 7th cycle based on the discharge capacity for each of the 2nd to 6th cycles, and update the capacity threshold range for the 7th cycle based on the calculated predicted minimum value and predicted maximum value. For example, if the predicted minimum value is calculated as C3 [Ah] and the predicted maximum value is calculated as C4 [Ah], the capacity threshold range can be updated to [C3 or more and C4 or less].

[0091] If the battery capacity for the 7th cycle falls outside the capacity threshold range for the 7th cycle, the battery may be classified as an abnormal battery. If the capacity for the 7th cycle falls within the capacity threshold range, the battery diagnostic device may perform a diagnostic process for the 8th cycle.

[0092] That is, the battery diagnostic device can update the capacity threshold range for the 8th cycle based on the discharge capacity for each of the 3rd to 7th cycles, as illustrated in Fig. 4(C). Here, the battery diagnostic device can calculate a predicted minimum value and a predicted maximum value for the capacity of the 8th cycle based on the discharge capacity for each of the 3rd to 7th cycles, and update the capacity threshold range for the 8th cycle based on the calculated predicted minimum value and predicted maximum value. For example, if the predicted minimum value is calculated as C5 [Ah] and the predicted maximum value is calculated as C6 [Ah], the capacity threshold range can be updated to [C5 or more and C6 or less].

[0093] The battery diagnostic device can determine whether the battery is abnormal by comparing the battery capacity for the 8th cycle with the capacity threshold range for the 8th cycle. Thereafter, the battery diagnostic device can sequentially update the capacity threshold range for each cycle and perform the diagnostic process, as described above.

[0094]

[0095] Figure 5 is a flowchart illustrating a method for defining a capacity threshold range according to an embodiment of the present invention. Below, a method for defining a capacity threshold range for the current charge / discharge cycle (the Tth cycle) is described.

[0096] The battery diagnostic device can collect battery capacities for each of the previous cycles (S510). Here, the battery diagnostic device can collect battery capacities for each of the most recent N charge / discharge cycles (TN cycle to T-1 cycle).

[0097] Thereafter, the battery diagnostic device can calculate a capacity reduction rate based on the collected battery capacities (S520).

[0098] In an embodiment, the battery diagnostic device can calculate a capacity reduction rate based on a difference value between the capacity of the earliest cycle (TN cycle) and the most recent cycle (T-1 cycle) among the most recent N cycles.

[0099] Here, the battery diagnostic device can calculate the capacity reduction rate (R_fall) based on the mathematical expression 1 below.

[0100] [Mathematical Formula 1]

[0101]

[0102] (Here, Cap(X) is the battery capacity for the Xth cycle)

[0103] In another embodiment, the battery diagnostic device can calculate a capacity reduction rate based on a difference value between the largest battery capacity and the smallest battery capacity among the battery capacities of the most recent N cycles.

[0104] Here, the battery diagnostic device can calculate the capacity reduction rate (R_fall) based on the mathematical expression 2 below.

[0105] [Equation 2]

[0106]

[0107] (Here, Capmax is the largest battery capacity among the battery capacities of the last N cycles, and Capmin is the smallest battery capacity among the battery capacities of the last N cycles)

[0108] The battery diagnostic device can calculate a predicted minimum value and a predicted maximum value for the capacity of the current cycle (cycle T) based on the capacity reduction rate calculated in S520 (S530). Here, the battery diagnostic device can calculate a predicted minimum value and a predicted maximum value based on the predicted value for the capacity reduction amount of the current cycle (cycle T).

[0109] More specifically, the battery diagnostic device can predict the minimum value of the capacity decrease based on the capacity and capacity decrease rate of the most recently performed M cycles (M is a natural number greater than or equal to 1 and less than N) among the most recent N cycles, and can predict the maximum value of the capacity decrease based on the capacity and capacity decrease rate of the first performed L cycles (L is a natural number greater than or equal to 1 and less than N) among the N cycles. Meanwhile, M and L can be predefined as appropriate numbers in consideration of the diagnostic accuracy.

[0110] In an embodiment, the minimum value of the capacity decrease may be calculated as a value obtained by multiplying the average or median value of the battery capacity of each of the M cycles recently performed (M is a natural number greater than or equal to 2 and less than N) by the capacity decrease rate. Here, the battery diagnostic device may calculate the minimum value (fall_min) of the capacity decrease based on the following mathematical expression 3.

[0111] [Equation 3]

[0112]

[0113] In an embodiment, the maximum value of the capacity decrease may be calculated as a value obtained by multiplying the average or median value of the battery capacity of each of the L cycles (L is a natural number greater than or equal to 2 and less than N) that have been performed previously by the capacity decrease rate. Here, the battery diagnostic device may calculate the maximum value (fall_max) of the capacity decrease based on the following mathematical expression 4.

[0114] [Equation 4]

[0115]

[0116] The battery diagnostic device can calculate a predicted minimum value and a predicted maximum value for the capacity of the current cycle (the Tth cycle) based on the predicted minimum value (fall_min) and maximum value (fall_max) of the predicted capacity decrease.

[0117] In an embodiment, the battery diagnostic device may calculate a predicted minimum value for the capacity of the current cycle (the Tth cycle) by subtracting the maximum value (fall_max) of the capacity decrease from the average or median value of the battery capacity of each of the M cycles recently performed (M is a natural number greater than or equal to 2 and less than N). Here, the battery diagnostic device may calculate a predicted minimum value (Cap_pre_min) for the capacity of the current cycle (the Tth cycle) based on the following mathematical expression 5.

[0118] [Equation 5]

[0119]

[0120] In an embodiment, the battery diagnostic device may calculate a predicted maximum value for the capacity of the current cycle (the Tth cycle) by subtracting the minimum value (fall_min) of the capacity decrease from the average or median value of the battery capacity of each of the L cycles that have been performed previously (L is a natural number greater than or equal to 2 and less than N). Here, the battery diagnostic device may calculate a predicted maximum value (Cap_pre_max) for the capacity of the current cycle (the Tth cycle) based on the following mathematical expression 6.

[0121] [Equation 6]

[0122]

[0123]

[0124] The battery diagnostic device can define a capacity threshold range for the current cycle (Cycle T) based on the predicted minimum value (Cap_pre_min) and predicted maximum value (Cap_pre_max) calculated in S530 (S540). Here, the capacity threshold range can be defined as being greater than or equal to the predicted minimum value (Cap_pre_min) and less than or equal to the predicted maximum value (Cap_pre_max).

[0125] The battery diagnostic device can check whether the charge / discharge cycle has ended (S550), and if not ended (N of S550), define a capacity threshold range for the T+1th cycle.

[0126]

[0127] FIG. 6 is a reference diagram illustrating a method for defining a capacity threshold range according to an embodiment of the present invention. Hereinafter, an example of a method for defining a capacity threshold range will be described with reference to FIG. 6.

[0128] The battery diagnostic device can define a capacity threshold range for the current cycle (the T-th cycle) based on the battery capacity for each of the last N charge / discharge cycles (the TN-th cycle to the T-1-th cycle). For example, as illustrated in FIG. 6, the battery diagnostic device can define a capacity threshold range for the current cycle (the T-th cycle) based on the discharge capacities (5, 4.8, 4.6, 4.4, 4.2) for each of the last five cycles.

[0129] First, the battery diagnostic device can calculate the capacity reduction rate (R_fall) based on the last five battery capacities. Here, the capacity reduction rate (R_fall) can be calculated as 0.16 according to the above mathematical equation 2.

[0130] Thereafter, the battery diagnostic device can calculate the minimum value (fall_min) of the capacity decrease based on the capacity and capacity decrease rate of the most recently performed M cycles, and can calculate the maximum value (fall_max) of the capacity decrease based on the capacity and capacity decrease rate of the previously performed L cycles. Here, the minimum value (fall_min) of the capacity decrease can be calculated as 0.688 according to the above mathematical expression 3, and the maximum value (fall_max) of the capacity decrease can be calculated as 0.784 according to the above mathematical expression 4.

[0131] Thereafter, the battery diagnostic device can calculate a predicted minimum value (Cap_pre_min) and a predicted maximum value (Cap_pre_max) for the capacity of the current cycle (the Tth cycle) based on the minimum value (fall_min) and the maximum value (fall_max) of the capacity decrease. Here, the predicted minimum value (Cap_pre_min) can be calculated as 3.516 according to the above mathematical expression 5, and the predicted maximum value (Cap_pre_max) can be calculated as 4.212 according to the above mathematical expression 6.

[0132] The battery diagnostic device can define the capacity critical range for the current cycle (T-th cycle) as 3.516 or more and 4.212 or less based on the calculated predicted minimum value (Cap_pre_min) and predicted maximum value (Cap_pre_max).

[0133] A battery diagnostic device can determine whether a battery is abnormal based on whether the battery capacity of the current charge / discharge cycle (the T-th cycle) is within a capacity threshold range (3.516 or more, 4.212 or less). If the capacity exceeds the capacity threshold range, the battery is classified as an abnormal battery. If the capacity is within the capacity threshold range, the battery diagnostic device can define a capacity threshold range for the T+1-th cycle.

[0134]

[0135] Figure 7 is a block diagram of a battery diagnostic device according to an embodiment of the present invention.

[0136] A battery diagnostic device (700) according to an embodiment of the present invention can be included in a long-term charge / discharge test system.

[0137] A battery diagnostic device (700) may include at least one processor (710), a memory (720) that stores at least one command executed through the processor, and a transmission / reception device (730) that is connected to a network and performs communication.

[0138] The at least one command may include a command for collecting battery capacity per cycle, calculated during a charge / discharge cycle of the battery; a command for defining a capacity threshold range for a current charge / discharge cycle based on battery capacities for a predetermined number of cycles; and a command for determining whether the battery is abnormal based on whether the capacity for the current charge / discharge cycle of the battery is within the capacity threshold range.

[0139] The command for collecting the battery capacity per cycle may include a command for collecting the discharge capacity of the battery per cycle.

[0140] The command defining the above capacity threshold range may include a command defining the capacity threshold range for the current charge / discharge cycle based on the battery capacity for the most recent N charge / discharge cycles (where N is a natural number greater than or equal to 3).

[0141] The command defining the above capacity threshold range may include a command calculating a capacity reduction rate based on battery capacities of previous cycles; a command calculating a predicted minimum value and a predicted maximum value for the capacity of the current cycle based on the capacity reduction rate; and a command defining the capacity threshold range based on the predicted minimum value and the predicted maximum value.

[0142] The command for calculating the capacity reduction rate may include a command for calculating the capacity reduction rate based on the change rate of the capacity of the earliest cycle performed among the most recent N cycles (N is a natural number greater than or equal to 3) and the capacity of the most recent cycle performed.

[0143] The command for calculating the predicted minimum value and predicted maximum value for the capacity of the current cycle may include a command for predicting the minimum value of the capacity reduction amount based on the capacity of the most recently performed M cycles (M is a natural number greater than or equal to 1 and less than N) among the most recent N cycles (N is a natural number greater than or equal to 3) and the capacity reduction rate; a command for predicting the maximum value of the capacity reduction amount based on the capacity of the first performed L cycles (L is a natural number greater than or equal to 1 and less than N) among the N cycles and the capacity reduction rate; and a command for calculating the predicted minimum value and predicted maximum value based on the minimum and maximum values ​​of the capacity reduction amount.

[0144] The command for calculating the predicted minimum value and predicted maximum value for the capacity of the current cycle may include a command for calculating the predicted minimum value by subtracting the maximum value of the capacity reduction amount from the average value or median value for the capacity of the M cycles; and a command for calculating the predicted maximum value by subtracting the minimum value of the capacity reduction amount from the average value or median value for the capacity of the L cycles.

[0145] The command for determining whether the battery is abnormal may include a command for classifying the battery as an abnormal battery if the capacity for the current charge / discharge cycle is outside the capacity threshold range.

[0146] The command defining the above capacity threshold range may include a command for sequentially updating the capacity threshold range as charge / discharge cycles for the battery sequentially proceed. Here, the command for determining whether the battery is abnormal may include a command for determining whether the battery is abnormal based on the sequentially updated capacity threshold range.

[0147] The battery diagnostic device (700) may also include an input interface device (740), an output interface device (750), a storage device (760), etc. Each component included in the battery diagnostic device (700) may be connected by a bus (770) to communicate with each other.

[0148] Here, the processor (710) may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed. The memory (or storage device) may be comprised of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory may be comprised of at least one of a read-only memory (ROM) and a random access memory (RAM).

[0149]

[0150] The operations of the method according to an embodiment of the present invention can be implemented as a computer-readable program or code on a computer-readable recording medium. A computer-readable recording medium includes any type of recording device that stores data readable by a computer system. Furthermore, a computer-readable recording medium can be distributed across network-connected computer systems, allowing the computer-readable program or code to be stored and executed in a distributed manner.

[0151]

[0152] While some aspects of the present invention have been described in the context of a device, they may also represent a description of a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be described as a corresponding block or item or a feature of a corresponding device. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most significant method steps may be performed by such a device.

[0153] Although the present invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. At least one processor; and A memory comprising at least one instruction to be executed via at least one processor, At least one of the above commands, A command to collect cycle-by-cycle battery capacity, calculated during the battery's charge / discharge cycle; A command defining a capacity threshold range for the current charge / discharge cycle based on the battery capacity for a predetermined number of cycles; and A battery diagnostic device, comprising a command for determining whether the battery is abnormal based on whether the capacity for the current charge / discharge cycle of the battery is within the capacity threshold range.

2. In claim 1, The command to collect the battery capacity per cycle is: A battery diagnostic device comprising a command for collecting cycle-by-cycle discharge capacity of the battery.

3. In claim 1, The command defining the above capacity threshold range is: A battery diagnostic device, comprising a command defining a capacity threshold range for a current charge / discharge cycle based on battery capacity for the most recent N charge / discharge cycles (where N is a natural number greater than or equal to 3).

4. In claim 1, The command defining the above capacity threshold range is: A command to calculate the capacity reduction rate based on the battery capacity of previous cycles; A command for calculating a predicted minimum value and a predicted maximum value for the capacity of the current cycle based on the above capacity reduction rate; and A battery diagnostic device, comprising a command defining the capacity threshold range based on the predicted minimum value and the predicted maximum value.

5. In claim 4, The command to calculate the above capacity reduction rate is: A battery diagnostic device, comprising a command for calculating a capacity decrease rate based on a difference value between the largest battery capacity and the smallest battery capacity among the battery capacities of the most recent N cycles (N is a natural number greater than or equal to 3).

6. In claim 4, The command to calculate the predicted minimum and maximum values ​​for the capacity of the current cycle is: A command to predict the minimum value of the capacity reduction amount based on the capacity of the most recent M cycles (M is a natural number greater than or equal to 1 and less than N) among the most recent N cycles (N is a natural number greater than or equal to 3) and the capacity reduction rate; A command for predicting the maximum value of the capacity reduction amount based on the capacity of L cycles (L is a natural number greater than or equal to 1 and less than N) that have been performed first among the N cycles above and the capacity reduction rate; and A battery diagnostic device, comprising a command for calculating the predicted minimum value and the predicted maximum value based on the minimum value and the maximum value of the capacity reduction amount.

7. In claim 6, The command to calculate the predicted minimum and maximum values ​​for the capacity of the current cycle is: A command for calculating the predicted minimum value by subtracting the maximum value of the capacity reduction from the average value or median value of the capacity of the M cycles; and A battery diagnostic device, comprising a command for calculating the predicted maximum value by subtracting the minimum value of the capacity reduction from the average value or median value of the capacity of the L cycles.

8. In claim 1, The command to determine whether the above battery is abnormal is: A battery diagnostic device, comprising a command for classifying the battery as an abnormal battery if the capacity for the current charge / discharge cycle is outside the capacity threshold range.

9. In claim 3, The command defining the above capacity threshold range is: As the charge and discharge cycles for the above battery are sequentially performed, a command is included for sequentially updating the capacity threshold range, The command to determine whether the above battery is abnormal is: A battery diagnostic device, comprising a command for determining whether the battery is abnormal based on a sequentially updated capacity critical range.

10. A step of collecting battery capacity per cycle, calculated during the charge / discharge cycle of the battery; A step of defining a capacity threshold range for the current charge / discharge cycle based on the battery capacity for a predetermined number of cycles; and A battery diagnostic method, comprising the step of determining whether the battery is abnormal based on whether the capacity for the current charge / discharge cycle of the battery is within the capacity threshold range.

11. In claim 10, The step of collecting the battery capacity per cycle is as follows: A battery diagnostic method, comprising the step of collecting cycle-by-cycle discharge capacity of the battery.

12. In claim 10, The step of defining the above capacity critical range is: A battery diagnosis method, comprising the step of defining a capacity critical range for a current charge / discharge cycle based on the battery capacity for the most recent N (where N is a natural number greater than or equal to 3) charge / discharge cycles.

13. In claim 10, The step of defining the above capacity critical range is: A step of calculating a capacity reduction rate based on the battery capacity of previous cycles; A step of calculating a predicted minimum value and a predicted maximum value for the capacity of the current cycle based on the above capacity reduction rate; and A battery diagnosis method, comprising a step of defining the capacity critical range based on the predicted minimum value and the predicted maximum value.

14. In claim 13, The step of calculating the above capacity reduction rate is: A battery diagnosis method, comprising a step of calculating a capacity decrease rate based on a difference value between the largest battery capacity and the smallest battery capacity among the battery capacities of the most recent N cycles (N is a natural number greater than or equal to 3).

15. In claim 13, The step of calculating the predicted minimum and maximum values ​​for the capacity of the current cycle is as follows: A step of predicting the minimum value of the capacity reduction amount based on the capacity of the most recent M cycles (M is a natural number greater than or equal to 1 and less than N) among the most recent N cycles (N is a natural number greater than or equal to 3) and the capacity reduction rate; A step of predicting the maximum value of the capacity reduction amount based on the capacity of L cycles (L is a natural number greater than or equal to 1 and less than N) that have been performed first among the N cycles above and the capacity reduction rate; and A battery diagnosis method, comprising a step of calculating the predicted minimum value and the predicted maximum value based on the minimum value and the maximum value of the capacity reduction amount.

16. In claim 15, The step of calculating the predicted minimum and maximum values ​​for the capacity of the current cycle is as follows: A step of calculating the predicted minimum value by subtracting the maximum value of the capacity reduction amount from the average value or median value of the capacity of the M cycles; and A battery diagnosis method, comprising a step of calculating the predicted maximum value by subtracting the minimum value of the capacity reduction from the average value or median value of the capacity of the L cycles.

17. In claim 10, The steps for determining whether the above battery is abnormal are: A battery diagnostic method, comprising the step of classifying the battery as an abnormal battery if the capacity for the current charge / discharge cycle is outside the capacity threshold range.

18. In claim 12, The step of defining the above capacity critical range is: A step of sequentially updating the capacity critical range as the charge / discharge cycle for the battery is sequentially performed, The steps for determining whether the above battery is abnormal are: A battery diagnosis method, comprising a step of determining whether the battery is abnormal based on a sequentially updated capacity critical range.

Images