Battery diagnosis device, battery diagnosis method, and battery diagnosis system

Abstract

According to some embodiments disclosed herein, a battery diagnostic device includes an interface configured to obtain first battery measurement values from a battery under diagnosis, and a controller configured to select second battery measurement values satisfying an analysis condition from among the first battery measurement values, calculate battery deviation values based on representative values of the second battery measurement values and differences between each of the second battery measurement values, calculate an analysis index value indicating a change trend of the battery deviation values based on measurement times, and diagnose a state of the battery under diagnosis based on the analysis index value.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2024-0131496, filed on September 27, 2024, with the Korean Ministry of Intellectual Property and Resources, and Korean Patent Application No. 10-2023-0180591, filed on December 13, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to a battery diagnostic device, a battery diagnostic method, and a battery diagnostic system. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively underway. In this paper, rechargeable batteries, as rechargeable / dischargeable batteries, can be interpreted as including all conventional nickel (Ni) / cadmium (Cd) batteries, nickel / metal hydride (MH) batteries, and more recently, lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries can achieve higher energy densities than traditional Ni / Cd and Ni / MH batteries, and can be manufactured in a smaller and lighter form, making them highly usable for powering mobile devices. Recently, with the expansion of lithium-ion battery applications into electric vehicle power supplies, lithium-ion batteries are attracting attention as a next-generation energy storage medium.

[0005] Battery cell voltage data can be used to diagnose battery condition. For example, condition diagnosis can be performed by calculating the voltage deviation, which indicates the difference between the cell voltage and the average cell voltage, and determining whether the voltage deviation meets specific diagnostic criteria. However, this diagnostic approach may require extensive calculations to determine whether specific diagnostic criteria are met, and the battery condition can be diagnosed based on a point-in-time rather than long-term behavior. Summary of the Invention

[0006] Technical issues

[0007] The embodiments disclosed herein aim to provide a battery diagnostic device, battery diagnostic method, and battery diagnostic system, wherein the state of the battery can be diagnosed based on long-term behavior without requiring complex calculations.

[0008] The technical problems of the embodiments disclosed herein are not limited to those described above, and those skilled in the art will clearly understand from the following description other unmentioned technical problems.

[0009] Technical solution

[0010] According to some embodiments disclosed herein, a battery diagnostic device includes: an interface configured to obtain a first battery measurement value from a target battery cell; and a controller configured to: select a second battery measurement value from the first battery measurement value that meets analysis conditions, calculate a battery deviation value based on a representative value of the second battery measurement value and the difference between each second battery measurement value, calculate an analysis index value indicating the trend of the battery deviation value according to the measurement time, and diagnose the state of the target battery cell based on the analysis index value.

[0011] According to some implementations, the controller can also be configured to: select an open-circuit voltage (OCV) measurement value from the first battery measurement value; and select an OCV measurement value above a threshold voltage value from the OCV measurement values ​​as a second battery measurement value.

[0012] According to some implementations, the controller can also be configured to calculate a moving average index indicating the long-term behavior of the battery deviation value over the duration of the measurement.

[0013] According to some implementations, the controller may also be configured to calculate an exponential moving average (EMA) of the battery deviation value to indicate long-term behavior, and the long-term behavior includes the behavior of the battery deviation value over a period of at least twice the analysis period of the EMA.

[0014] According to some implementations, the controller may be further configured to: calculate the slope of the deviation value based on the change in EMA over a period of at least twice the analysis period; and diagnose the state of the target cell based on the slope of the deviation value.

[0015] According to some implementations, the controller may be further configured to: detect a blank period in the acquisition period for obtaining the first battery measurement value where there is no second battery measurement value that meets the analysis conditions; and estimate the analysis indication value corresponding to the blank period by performing interpolation based on the analysis indication value.

[0016] According to some implementations, the target cell for diagnosis may be included in the target battery for diagnosis, and the target battery for diagnosis may be installed on a mobile device.

[0017] According to some embodiments disclosed herein, a battery diagnostic method includes the following steps: obtaining a first battery measurement value from a target battery cell; selecting a second battery measurement value from the first battery measurement value that meets the analysis conditions; calculating a battery deviation value based on the difference between a representative value of the second battery measurement value and each second battery measurement value; calculating an analytical index value indicating the trend of the battery deviation value according to the measurement time; and diagnosing the state of the target battery cell based on the analytical index value.

[0018] According to some implementations, the step of selecting a second battery measurement value may include: selecting an open-circuit voltage (OCV) measurement value from the first battery measurement values; and selecting an OCV measurement value that is at least a threshold voltage value from the OCV measurement values ​​as the second battery measurement value.

[0019] According to some implementations, the step of calculating and analyzing index values ​​may include: calculating a moving average index indicating the long-term behavior of the battery deviation value over the measurement period.

[0020] According to some implementations, the step of calculating the moving average indicator may include: calculating an exponential moving average (EMA) of the battery deviation value to indicate long-term behavior, and the long-term behavior may include the behavior of the battery deviation value over a period of at least twice the analysis period of the EMA.

[0021] According to some implementations, the steps of diagnosing the state of a target battery cell may include: calculating a deviation slope based on the change in EMA over a period of at least twice the analysis period; and diagnosing the state of the target battery cell based on the deviation slope.

[0022] According to some implementations, the step of calculating the analytical index value may include: detecting a blank period in the acquisition period used to obtain the first battery measurement value where there is no second battery measurement value that meets the analysis conditions; and estimating the analytical index value corresponding to the blank period by performing interpolation based on the analytical index value.

[0023] According to some implementations, the steps of diagnosing a target battery cell may include: diagnosing a target battery, and the target battery may be installed on a mobile device.

[0024] According to some embodiments disclosed herein, a battery diagnostic system includes: a target battery for diagnosis, comprising a target cell for diagnosis and installed on an electrical device; and a battery diagnostic device configured to: obtain a first battery measurement value from the target cell for diagnosis; select a second battery measurement value from the first battery measurement value that meets analysis conditions; calculate a battery deviation value based on a representative value of the second battery measurement value and the difference between each second battery measurement value; calculate an analytical index value indicating the trend of the battery deviation value according to the measurement time; and diagnose the state of the target cell for diagnosis based on the analytical index value.

[0025] Beneficial effects

[0026] According to the embodiments disclosed herein, a battery diagnostic device, a battery diagnostic method, and a battery diagnostic system can be provided that can diagnose the state of a battery based on long-term behavior without requiring complex calculations.

[0027] The technical effects of the embodiments disclosed in this document are not limited to the effects described above, and those skilled in the art will clearly understand other effects not mentioned based on the disclosure of this document. Attached Figure Description

[0028] Figure 1 The components of a battery diagnostic system according to some embodiments are shown.

[0029] Figure 2 The components of a battery diagnostic device according to some embodiments are shown.

[0030] Figure 3 This demonstrates the diagnostic process for anomalies in a target battery based on conventional techniques.

[0031] Figure 4 This illustrates the abnormal diagnosis process for a target battery according to some implementation methods.

[0032] Figure 5 and Figure 6 The long-term behavior of the target battery cell is shown according to some implementation methods.

[0033] Figure 7 The analysis process of the exponential moving average (EMA) according to some implementation methods is shown.

[0034] Figure 8 This paper illustrates a scheme for diagnosing defects in battery cells by using long-term behavioral gradients, according to some implementation methods.

[0035] Figure 9 This illustrates a scheme for artificially inducing a short circuit in a battery cell according to some embodiments.

[0036] Figure 10 The correlation between the 240-day slope and artificially defective cells is shown according to some implementations.

[0037] Figure 11 The operation of a battery diagnostic method according to some embodiments is shown. Detailed Implementation

[0038] The embodiments disclosed herein will be described in detail below with reference to the accompanying drawings. However, this description is not intended to limit the disclosure of this document to the specific embodiments, and it should be construed as including various variations, equivalents, and / or alternatives to the embodiments described herein.

[0039] It should be understood that the embodiments described herein and the terminology used therein are not intended to limit the technical features set forth herein to a particular embodiment, but rather to include various variations, equivalents, or substitutions of the corresponding embodiments. Regarding the description of the drawings, similar reference numerals may be used to denote the same or related elements. It should be understood that, unless the relevant context clearly indicates otherwise, the singular form of a noun corresponding to an item may include one or more things.

[0040] As used herein, each of phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B or C” can include any one or all possible combinations of the items listed together in the corresponding phrase. Unless otherwise stated, terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” can be used simply to distinguish one component from another and do not otherwise limit the components (e.g., in terms of importance or order).

[0041] In this document, it should be understood that when an element (e.g., a first element) is referred to as being “connected,” “linked,” or “coupled” to another element (e.g., a second element), whether or not the terms “operably” or “communicably” are used, it means that the element can be connected to the other element directly (e.g., wired or wirelessly) or indirectly (e.g., via a third element).

[0042] Methods according to various embodiments disclosed herein can be included and provided in a computer program product. The computer program product can be traded as a product between a seller and a buyer. In the case of online distribution, at least a portion of the computer program product can be stored at least temporarily in a machine-readable storage medium (e.g., the memory of a manufacturer's server, an app store's server, or a relay server), or can be temporarily generated.

[0043] According to the embodiments disclosed herein, each of the above-described components (e.g., modules or programs) may include a single entity or multiple entities, some of which may be disposed separately on other components. According to the embodiments disclosed herein, one or more of the above-described components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into one component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as the corresponding components in the multiple components prior to integration. According to the embodiments disclosed herein, operations performed by modules, programs, or other components may be performed sequentially, in parallel, repeatedly, or heuristically, or one or more operations may be performed in a different order or omitted, or one or more other operations may be added.

[0044] Figure 1 The components of a battery diagnostic system according to some embodiments are shown.

[0045] Reference Figure 1 The battery diagnostic system 10 may include a power device 110, a target battery 120 for diagnostics, a battery diagnostic device 130, and a management server 140. However, it is not limited to this; some components may be omitted from the battery diagnostic system 100, or the battery diagnostic system 100 may also include other general components.

[0046] The battery diagnostic system 100 can refer to a system that diagnoses a target battery 120. The target battery 120 can be discharged or charged by the electrical device 110, and the battery diagnostic device 130 can diagnose the target battery 120 by analyzing data on its charging and discharging.

[0047] Electrical device 110 can use diagnostic target battery 120 as a power source. According to one embodiment, electrical device 110 may include a mobile device such as an electric vehicle (EV), a hybrid electric vehicle (HEV), an electric bicycle, etc. For example, the mobile device can discharge the diagnostic target battery 120 to drive a motor and charge the diagnostic target battery 120 through regenerative braking.

[0048] The diagnostic target battery 120 may include one or more battery packs. The battery packs of the diagnostic target battery 120 may include multiple battery modules, and each battery module may include multiple battery cells. According to one embodiment, the diagnostic target battery 120 may be installed on an electrical device 110.

[0049] The battery diagnostic device 130 can perform diagnostic operations on the target battery 120. The battery diagnostic device 130 can measure battery data from the target battery 120 and perform diagnostics on the cells, modules, battery packs, etc., of the target battery 120 based on the battery data. According to one embodiment, the battery diagnostic device 130 can diagnose whether defects leading to low voltage energy or battery fire have occurred in the target battery 120 on a cell, module, or battery pack basis. According to one embodiment, the battery diagnostic device 130 can be a battery management system (BMS) configured together with the target battery 120.

[0050] According to one embodiment, the battery diagnostic device 130 may include a BMS configured in an in-vehicle manner to diagnose the target battery 120 and / or external devices arranged remotely from the target battery 120 in a non-in-vehicle manner. The external devices may include a charger from a charging station, a battery diagnostic device, a cloud computing server, etc.

[0051] The management server 140 can manage the diagnostic process and results of the battery diagnostic device 130. For example, the management server 140 can be a cloud computing server. The management server 140 can exchange data with the battery diagnostic device 130 via wired / wireless communication. When battery data is measured from the target battery 120 or a defect is diagnosed, the corresponding result can be sent to the management server 140 and recorded in a database. According to one embodiment, the management server 140 can receive data for battery diagnostics and perform the operation of diagnosing the target battery 120 on behalf of the battery diagnostic device 130. On the other hand, the battery diagnostic device 130 can perform diagnostic operations by executing battery management software, and the management server 140 can provide the battery diagnostic device 130 with update information for the battery management software.

[0052] Figure 2 The components of a battery diagnostic device according to some embodiments are shown.

[0053] Reference Figure 2 The battery diagnostic device 130 may include an interface 131 and a controller 132. However, it is not limited to this; some components may be omitted from the battery diagnostic device 130, or the battery diagnostic device 130 may also include other general components.

[0054] According to one embodiment, the interface 131 and controller 132 of the battery diagnostic device 130 can be electrically connected to each other in a device-to-device communication manner. The device-to-device communication manner may include general purpose input / output (GPIO), serial peripheral interface (SPI), mobile industrial processor interface (MIPI), etc.

[0055] Interface 131 can obtain battery data from the target battery 120 for diagnosis. According to one embodiment, interface 131 may include a communication unit configured to receive battery data and / or a sensor unit configured to measure battery data. According to one embodiment, when the battery diagnostic device 130 is implemented in a non-vehicle-mounted form, the communication unit can receive battery data via wired data communication, wireless data communication, or other methods. When the battery diagnostic device 130 is implemented in a vehicle-mounted form, the sensor unit can be configured to measure values ​​such as voltage, current, temperature, and resistance from the target battery 120 for diagnosis.

[0056] For example, the sensor unit of interface 131 can be configured to generate various battery measurements from the target battery 120 for diagnosis. For this purpose, the sensor unit may include measuring devices such as voltmeters, ammeters, thermometers, etc.

[0057] The controller 132 may have a structure for executing instructions that implement the operations of the battery diagnostic device 130. The controller 132 may be implemented using an array of multiple logic gates or a general-purpose microprocessor for handling various operations, and may include a single processor or multiple processors. For example, the controller 132 may be implemented as at least one of a microprocessor, CPU, GPU, and AP.

[0058] The controller 132 can operate in conjunction with a memory configured to store various data, instructions, mobile applications, computer programs, etc. The memory can be configured separately from or integrated with the controller 132. The controller 132 can be implemented to execute instructions stored in the memory to perform various calculations. For example, the memory can be implemented as a non-volatile memory device such as ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, RRAM, FRAM, etc., or as a volatile memory device such as DRAM, SRAM, SDRAM, PRAM, etc., or as a form such as HDD, SSD, SD, microSD, etc., or a combination thereof.

[0059] Interface 131 can be configured to obtain first battery measurements from a target diagnostic cell. The target diagnostic cell may be included in the target diagnostic battery 120. The first battery measurements may include measurements of cell current and cell voltage at a given measurement time point. Each first battery measurement may correspond to a specific target diagnostic cell, and the first battery measurements may be collected continuously at each measurement interval to form time-series data.

[0060] According to one embodiment, when the target battery 120 is charged or discharged by the device 110, the first battery measurement value may include the current value and voltage value measured from the target battery cell.

[0061] The controller 132 can be configured to select a second battery measurement value that meets analysis conditions from the first battery measurement values. Analysis conditions can be applied to select values ​​from the first battery measurement values ​​for diagnosis by the battery diagnostic device 130. Analysis conditions can be set based on current and voltage values. According to one embodiment, OCV data with a current value of 0 and / or data with a voltage value of at least a specific value can be selected as the second battery measurement value.

[0062] The controller 132 can be configured to calculate a battery deviation value based on a representative value of the second battery measurements and the difference between each second battery measurement. The second battery measurements may each correspond to a diagnostic target cell, and a representative value (such as an average or median value) of the second battery measurements can be calculated. By subtracting the representative value from each second battery measurement, the battery deviation value corresponding to each diagnostic target cell can be calculated. According to one embodiment, a deviation value corresponding to the OCV value of each diagnostic target cell can be calculated.

[0063] The controller 132 can be configured to calculate an analytical indicator that shows the trend of battery deviation values ​​over measurement time. Similar to the first battery measurement value described above, the second battery measurement value and the battery deviation value can be time series data with values ​​at predetermined intervals. For example, each battery deviation value can be implemented as a chart with new values ​​at predetermined intervals. The analytical indicator that shows the trend of each battery deviation value can be a technical analysis indicator such as a moving average (MA), EMA, etc.

[0064] The controller 132 can be configured to diagnose the state of a target cell based on analytical metrics. For example, the state of the corresponding cell can be diagnosed by analyzing the long-term behavior or trend of the analytical metrics for each target cell. According to one embodiment, the long-term behavior or trend of the analytical metrics can be expressed as a rate of change or a graph slope relative to a target time period (e.g., 100 days, 240 days, etc.) of the corresponding metric.

[0065] According to one embodiment, controller 132 can be configured to select, from a first battery measurement value, an open-circuit voltage (OCV) measurement value corresponding to a battery current of 0, and from the OCV measurement value, a second battery measurement value corresponding to a battery voltage at least a threshold voltage. The voltage corresponding to a battery current of 0 can correspond to the OCV value measured in the open-circuit state. For a cell with a maximum voltage of 4.3V, the threshold voltage can be set to 3.9V or 4.12V, and the OCV measurement value having a voltage at least the threshold voltage can be selected as the second battery measurement value. When no measurement value meets the current and / or voltage conditions during a specific measurement period, the second battery measurement value may not be selected for the corresponding measurement period. To resolve such data gaps, data interpolation can be used as described below.

[0066] According to one embodiment, controller 132 can be configured to calculate a moving average index indicating the long-term behavior of battery deviation values ​​over measurement time. The moving average (MA) can also be called a rolling average, learning average, etc. The MA index considers recent data changes along with previous data changes to reflect long-term behavior. The analysis period for MA can be set to 3 days, 5 days, 10 days, 15 days, 20 days, 30 days, 50 days, 100 days, 120 days, 150 days, 200 days, 240 days, 300 days, or other values. According to one embodiment, long-term behavior can refer to behavior that considers previous measurements along with other measurements rather than just the most recent measurements, and can be selected from schemes that diagnose the target battery 120 based on measurements at measurement time points.

[0067] According to one implementation, controller 132 can be configured to calculate the EMA of the battery deviation value to indicate long-term behavior, wherein long-term behavior can include the behavior of the battery deviation value over a period at least twice the analysis period of the EMA. The type of MA indicator can include simple MA, cumulative MA, weighted MA, etc., but battery diagnostic device 130 can use EMA. For example, when the analysis period of the EMA is 10 days, a new EMA value can be calculated every 10 days. In this case, the rate of change (slope) of the EMA value over 20 days, at least twice the 10 days, can indicate long-term behavior. 30 days, 50 days, 80 days, 100 days, 120 days, 150 days, 200 days, 240 days, 300 days, or other values ​​can be used instead of 20 days.

[0068] According to one implementation, when the analysis period is 10 days, the new battery deviation value Deviation_new can be reflected as a 5% rate, and the previous EMA value EMA_Deviation_old can be reflected as a 95% rate, thereby calculating the new EMA value. This will be referred to the following... Figure 7The following description is provided. According to one implementation, other values ​​such as 1%, 2%, 3%, 7%, 8%, 10%, 12%, and 15% can be used instead of 5%.

[0069] According to one implementation, controller 132 can be configured to calculate a deviation slope based on the change in EMA over a period at least twice the analysis period, and to diagnose the condition of the target cell based on the deviation slope. For example, when the analysis period is 10 days, the change (slope) in EMA values ​​over at least 20 days (e.g., 100 days or 240 days) can be calculated, and the corresponding slope value can be compared with a reference range to diagnose the condition of the target cell. For example, when the absolute value of the slope exceeds a threshold in some cells, the corresponding cell can be diagnosed as defective.

[0070] According to one embodiment, the controller 132 can be configured to: detect a gap period in which no second battery measurement value satisfying the analysis conditions exists during the acquisition period for obtaining the first battery measurement value, and perform interpolation based on the analysis index value to estimate the analysis index value corresponding to the gap period. The acquisition period for obtaining the first battery measurement value can be the measurement period for collecting the first battery measurement value. For example, the first measurement period can be a gap period where no second battery measurement value satisfying the analysis conditions exists when there is no value in the measurements of the first measurement period where the current value is 0 and the voltage value is at least 3.9V. For the gap period, there are no second battery measurement values, battery deviation values, EMA values, etc., so interpolation can be used to estimate them. According to one embodiment, linear interpolation can be used to estimate the values ​​in the gap period.

[0071] According to one embodiment, the diagnostic target cell may be included in a diagnostic target battery 120, which may be mounted on an electrical device 110. The electrical device 110 may include a mobile device powered by the diagnostic target battery 120. For example, the mobile device may include an EV, a HEV, an electric bicycle, etc. The mobile device may discharge the diagnostic target battery 120 to drive a motor and charge the diagnostic target battery 120 through regenerative braking. During this process, first battery measurements of the diagnostic target battery 120 may be collected periodically.

[0072] Figure 3 This demonstrates the diagnostic process for anomalies in a target battery based on conventional techniques.

[0073] Reference Figure 3 This can be illustrated by conventional technique 300 for diagnosing anomalies in the target battery 120. In conventional technique 300, anomalies in the target battery 120 can be diagnosed via operations 310 to 340.

[0074] In operation 310, battery data regarding time, voltage, current, balancing time, and SOHC can be collected. In this case, additional processing information such as balancing time and SOHC may be required. In operation 320, an OCV value greater than 3.9V can be obtained. To extract the OCV value, it can be determined whether the current value is 0.

[0075] In operation 330, an OCV deviation value based on the average OCV can be calculated. In operation 340, activation conditions for diagnosing anomalies in the target battery 120 based on the OCV deviation value, etc., can be determined. To determine whether the activation conditions are met, in addition to calculating the OCV deviation value, a process can be performed to determine whether the difference between the maximum and minimum SOHC values ​​of the cell falls within a specific range. Therefore, conventional technology 300 may involve a slightly more complex battery diagnostic process.

[0076] Figure 4 This illustrates the abnormal diagnosis process for a target battery according to some implementation methods.

[0077] Reference Figure 4 The process of diagnosing abnormalities in the target battery 120 may include operations 410 to 460.

[0078] In operation 410, data such as time, voltage, and current can be collected. Compared to operation 310 of conventional technology 300, it eliminates the need for additional processing steps such as balancing time and SOHC. In operation 420, an OCV value exceeding 3.9V can be obtained; for this purpose, a voltage value with a current of 0 can be compared to 3.9V. In operation 430, the OCV average value can be used to calculate the OCV deviation value.

[0079] In operation 440, an EMA filter can be applied to the OCV deviation value. For example, an EMA value with a 10-day analysis period can be calculated, and a chart plotting the EMA value updated every 10 days can be generated for each cell. According to one implementation, an EMA filter can be applied that reflects 5% as the new EMA value and 95% as the existing EMA value.

[0080] In operation 450, interpolation can be performed on the EMA value of the battery deviation. For example, in the calculation of the slope in the graph of the deviation EMA value, interpolation can be performed to compensate for the omission when the deviation EMA value is missed at a specific point in time. In operation 420, the deviation EMA value may be missed when there is no measurement value with a current value of 0 and a voltage value of 3.9V.

[0081] In Operation 460, the slope can be calculated in a graph of the deviation EMA values. For example, the slope can be calculated based on the change in the deviation EMA values ​​over 240 days, and the slope can be compared with diagnostic thresholds to diagnose battery defects.

[0082] Figure 5 and Figure 6 The long-term behavior of the target battery cell is shown according to some implementation methods.

[0083] Reference Figure 5 and Figure 6 It can display graphs indicating long-term behavior in normal battery cells and graphs indicating long-term behavior in abnormal battery cells.

[0084] Figure 5 This shows 14 battery deviation values ​​for 14 cells in a battery module, including cells 1 through 14. The battery deviation values ​​can be OCV deviation values. Figure 5 The battery module can include 14 normal cells, and the battery deviation value 510 of the normal cells can all be close to 0. For example, as a result of calculating the OCV deviation value of a specific cell every 10 days, low values ​​such as 0.5mV, 1.0mV, and 0.9mV can be observed.

[0085] Figure 6 The diagram shows 14 battery deviation values ​​for 14 cells in a battery module, including cells 57 through 70. The battery deviation value 620 of the abnormal cell among the 14 cells may deviate significantly from other values. For example, as a result of calculating the OCV deviation value of a specific cell every 10 days, higher values ​​such as 8.3mV, 12.5mV, and 11.2mV can be observed. On the other hand, the battery deviation value 610 of the 13 normal cells can remain close to 0.

[0086] Figure 7 This illustrates the process of analyzing the exponential moving average (EMA) according to some implementation methods.

[0087] Reference Figure 7 This can display pseudocode illustrating the process of analyzing EMA. The seven procedures of the pseudocode can correspond to... Figure 4 Operations 410 to 460.

[0088] In process 1, an OCV value with a current of 0 can be selected from all measured values, and a value exceeding 4.12V can also be selected from the OCV values. The 4.12V reference value can be changed to other values ​​such as 3.9V. In process 2, the last value of each day can be specified as the value representing that day. In processes 3 and 4, the deviation value based on the average value can be calculated.

[0089] In process 5, an EMA can be applied to the deviation value. For example, for a 10-day analysis period, the current deviation value and the EMA value from 10 days ago can be used to calculate the current EMA value. According to one implementation, the ratio reflecting the current deviation value can be 5%, and can be changed to another value based on the battery diagnostic design.

[0090] In process 6, when there is missing data that does not meet the conditions of process 1, data interpolation (which can be linear interpolation) can be performed to compensate for the missing data. In process 7, the slope of the 240-day EMA value can be calculated, and the calculated slope can be used to diagnose the presence of defective cells.

[0091] Figure 8 This paper illustrates a scheme for diagnosing defects in battery cells by using long-term behavioral gradients, according to some implementation methods.

[0092] Reference Figure 8 It can display a graph indicating the results of long-term observation of the EMA value of battery cells.

[0093] Figure 8 The long-term behavior of EMA values ​​for more than 8 battery cells is shown. For cell 61 devVcell_61, the EMA slope may be greater than that of other normal cells, considering the long-term trend.

[0094] For example, unlike other normal cells, cell 61 (devVcell_61) may have experienced an EMA value change of approximately 8mV over a period of about 240 days, from January 2022 to May 2023. An 8mV change may exceed the threshold for defect diagnosis. For example, the threshold for defect diagnosis could be set to 3mV, 4mV, 5mV, 6mV, or other suitable values ​​based on 240 days.

[0095] Figure 9 This illustrates a scheme for artificially inducing a short circuit in a battery cell according to some embodiments.

[0096] Reference Figure 9 The diagram shows electrode plate 910 and separator 920 to illustrate a scheme for artificially inducing a short circuit in a battery cell. Electrode connector 911 may be formed on electrode plate 910.

[0097] In order to create a defect in the battery cell by inducing a short circuit between the two electrode plates separated by the separator 920, an artificial damage 921 can be formed on the separator 920. For example, the damage 921 can be formed as having a 2mm diameter at 70mm from the lower end and 70mm from the right end of the separator 920. The size is 2mm. By using a battery cell with artificial damage 921 formed on it, the performance of a diagnostic scheme for long-term behavior using battery diagnostic device 130 can be verified.

[0098] Figure 10 The correlation between the 240-day slope and artificially defective cells is shown according to some implementations.

[0099] Reference Figure 10 This shows how by... Figure 9 A table comparing the performance of various defect diagnosis schemes by marking battery cells that have defects.

[0100] Figure 10 The table can indicate the correlation between various diagnostic criteria and the "Label" of a battery cell with human-caused defects. According to one implementation, the correlation in the table can be expressed as a Pearson correlation coefficient. The correlation of positive variables may be low for values ​​close to 0 and high for values ​​close to ±1.

[0101] exist Figure 10 In the diagnostic criteria, slope criterion 1010 can be associated with battery diagnostics using an EMA slope relative to the battery deviation value. Within slope criterion 1010, a first criterion 1011 with a 100-day analysis period and a second criterion 1012 with a 240-day analysis period can be associated with battery diagnostic schemes based on long-term behavior.

[0102] For the first standard 1011, the scheme using a 100-day EMA slope to diagnose cell defects may have a correlation of -0.47 with the actual defective cell "Label". Similarly, for the second standard 1012, the scheme using a 240-day EMA slope to diagnose cell defects may have a correlation of -0.54 with the actual defective cell "Label". Therefore, it can be seen that in the schemes for diagnosing battery defects based on long-term behavior, the scheme using a 240-day slope has higher performance than the scheme using a 240-day slope.

[0103] Figure 11 The operation of a battery diagnostic method according to some embodiments is shown.

[0104] Reference Figure 11 The battery diagnostic method may include operations 1110 to 1150. However, it is not limited to this; some operations may be omitted, other general operations may be added, and the operations of the battery diagnostic method may be performed in a different order than that shown.

[0105] The battery diagnostic method may include operations processed sequentially by the battery diagnostic device 130. Therefore, the content described above for the battery diagnostic device 130, even if omitted below, is equally applicable to the battery diagnostic method.

[0106] Operations 1110 to 1150 of the battery diagnostic method can be performed by the interface 131 and controller 132 of the battery diagnostic device 130.

[0107] In operation 1110, the battery diagnostic device 130 can obtain a first battery measurement value from the target battery cell. In operation 1120, the battery diagnostic device 130 can select a second battery measurement value that meets the analysis conditions from the first battery measurement value.

[0108] In operation 1130, the battery diagnostic device 130 can calculate a battery deviation value based on the difference between a representative value of the second battery measurement and each of the second battery measurements. In operation 1140, the battery diagnostic device 130 can calculate an analytical index value indicating the trend of the battery deviation value over measurement time. In operation 1150, the battery diagnostic device 130 can diagnose the condition of the target battery cell based on the analytical index value.

[0109] According to one embodiment, the battery diagnostic method can be implemented as a computer program stored in a computer-readable storage medium. That is, the computer program may include instructions for implementing the battery diagnostic method, and the instructions may be stored in the computer-readable storage medium. The computer program may include a mobile application.

[0110] According to one implementation, computer-readable storage media may include magnetic media (such as hard disks, floppy disks, and magnetic tapes), optical media (such as optical disc read-only memories (CD-ROMs) and digital versatile optical discs (DVDs)), magneto-optical media (such as magneto-optical discs), and hardware devices specifically configured to store and execute program instructions (such as ROMs, RAMs, and flash memory). Computer program instructions may include machine language code created by a compiler and high-level language code that can be executed by a computer using an interpreter.

[0111] Unless otherwise stated, terms such as “comprising,” “constituting,” or “having” above may indicate that the corresponding component may be inherent and should therefore be interpreted as including other components rather than excluding them. Unless otherwise defined, all terms, including technical or scientific terms, shall have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Commonly used terms (such as those defined in dictionaries) shall be interpreted as having the same meaning as in the context of the relevant art and shall not be interpreted as having an ideal or overly formal meaning unless expressly defined in this document.

[0112] The above description is merely an illustration of the technical ideas disclosed herein, and various modifications and variations can be made by those skilled in the art without departing from the essential characteristics of the embodiments disclosed herein. Therefore, the embodiments disclosed herein are intended to describe, not limit, the technical spirit of the embodiments disclosed herein, and the scope of the technical spirit of this disclosure is not limited by these embodiments. The scope of protection of the technical spirit disclosed herein should be interpreted through the following claims, and all technical spirit within the same scope should be understood to be included within the scope of this document.

[0113] [Explanation of reference numerals in the attached figures]

[0114] 100: Battery diagnostic system 110: Electrical equipment

[0115] 120: Target battery for diagnosis; 130: Battery diagnostic device

[0116] 131: Interface 132: Controller

[0117] 140: Management Server

Claims

1. A battery diagnostic device, the battery diagnostic device comprising: An interface configured to obtain a first battery measurement from a target battery cell for diagnostic purposes; as well as The controller is configured to: Select a second battery measurement value that meets the analysis conditions from the first battery measurement value; The battery deviation value is calculated based on the difference between the representative value of the second battery measurement and each second battery measurement value; Calculate the analytical index value indicating the trend of the battery deviation value according to the measurement time; as well as The condition of the target battery cell is diagnosed based on the analytical index values.

2. The battery diagnostic device according to claim 1, wherein, The controller is also configured to: Select the open-circuit voltage (OCV) measurement value from the first battery measurement value; and Select OCV measurement values ​​that are above the threshold voltage value from the OCV measurement values ​​as the second battery measurement value.

3. The battery diagnostic device according to claim 1, wherein, The controller is also configured to calculate a moving average index that indicates the long-term behavior of the battery deviation value over the measurement time.

4. The battery diagnostic device according to claim 3, wherein, The controller is also configured to calculate the exponential moving average (EMA) of the battery deviation value to indicate the long-term behavior, and The long-term behavior includes the behavior of the battery deviation value over a period of time at least twice the analysis period of the EMA.

5. The battery diagnostic device according to claim 4, wherein, The controller is also configured to: The slope of the deviation value is calculated based on the change of the EMA over a period at least twice the analysis period; and The condition of the target battery cell is diagnosed based on the slope of the deviation value.

6. The battery diagnostic device according to claim 1, wherein, The controller is also configured to: The system detects a blank period in the acquisition period used to obtain the first battery measurement value where there is no second battery measurement value that meets the analysis conditions. as well as The analytical indicator values ​​corresponding to the blank period are estimated by performing interpolation based on the analytical indicator values.

7. The battery diagnostic device according to claim 1, wherein, The target cell for diagnosis is included in the target battery for diagnosis, and the target battery for diagnosis is installed on the mobile device.

8. A battery diagnostic method, the battery diagnostic method comprising the following steps: Obtain the first battery measurement value from the target battery cell for diagnosis; Select a second battery measurement value that meets the analysis conditions from the first battery measurement value; The battery deviation value is calculated based on the difference between the representative value of the second battery measurement and each second battery measurement value; Calculate the analytical index value indicating the trend of the battery deviation value according to the measurement time; as well as The condition of the target battery cell is diagnosed based on the analytical index values.

9. The battery diagnostic method according to claim 8, wherein, The step of selecting the second battery measurement value includes the following steps: Select the open-circuit voltage (OCV) measurement value from the first battery measurement value; and Select an OCV measurement value that is at least a threshold voltage value from the OCV measurement values ​​as the second battery measurement value.

10. The battery diagnostic method according to claim 8, wherein, The steps in calculating and analyzing the index value include the following steps: calculating a moving average index that indicates the long-term behavior of the battery deviation value over the measurement time.

11. The battery diagnostic method according to claim 10, wherein, The steps for calculating the moving average indicator include the following: calculating the exponential moving average (EMA) of the battery deviation value to indicate the long-term behavior, and The long-term behavior includes the behavior of the battery deviation value over a period of time at least twice the analysis period of the EMA.

12. The battery diagnostic method according to claim 11, wherein, The steps for diagnosing the condition of the target battery cell include the following: The slope of the deviation value is calculated based on the change in the EMA over a period at least twice the analysis period; and The condition of the target battery cell is diagnosed based on the slope of the deviation value.

13. The battery diagnostic method according to claim 8, wherein, The steps for calculating and analyzing index values ​​include the following: The detection process identifies a blank period during which no second battery measurement value satisfying the analyzed conditions is found within the acquisition period used to obtain the first battery measurement value; and The analytical indicator values ​​corresponding to the blank period are estimated by performing interpolation based on the analytical indicator values.

14. The battery diagnostic method according to claim 8, wherein, The target cell for diagnosis is included in the target battery for diagnosis, and the target battery for diagnosis is installed on the mobile device.

15. A battery diagnostic system, the battery diagnostic system comprising: A diagnostic target battery, the diagnostic target battery comprising a diagnostic target cell and installed in an electrical device; as well as A battery diagnostic device is configured to obtain a first battery measurement value from a target battery cell, select a second battery measurement value that meets analysis conditions from the first battery measurement value, calculate a battery deviation value based on the difference between a representative value of the second battery measurement value and each second battery measurement value, calculate an analysis index value indicating the trend of the battery deviation value according to the measurement time, and diagnose the state of the target battery cell based on the analysis index value.

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