Battery diagnosis device and operation method thereof

Abstract

A battery diagnosis device according to an embodiment disclosed in the present document may comprise: an acquisition unit for acquiring open circuit voltage (OCV) data of multiple battery units; a deviation calculation unit for, on the basis of the OCV data, calculating an OCV change value of the multiple battery units in a designated time interval and calculating an OCV deviation value with respect to an average indicating a difference value between an average OCV change value of the multiple battery units and an OCV change value of a target battery unit among the multiple battery units; an exponential moving average (EMA) calculation unit for calculating an EMA deviation value by applying an EMA filter to the OCV deviation value with respect to the average of the target battery unit; and a diagnosis unit for diagnosing an abnormality of the target battery unit on the basis of the EMA deviation value.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority from Korean Patent Application No. 10-2023-0119152, filed September 7, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery diagnostic device and an operating method thereof.

[0005] Recently, research and development on secondary batteries has been actively conducted. Here, secondary batteries are rechargeable and include both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them suitable for use as power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Additionally, secondary batteries can be utilized as battery packs, which typically include battery modules in which multiple battery cells are connected in series and / or parallel. Furthermore, secondary batteries can be utilized as battery racks, which include multiple battery modules and a rack frame that accommodates these battery modules.

[0007] Battery cells, battery modules, battery packs, or battery racks like these can be utilized in a variety of devices. For example, batteries can be used in mobile devices such as cell phones, laptops, smartphones, and tablets, as well as in electric vehicles (EVs, HEVs, PHEVs) and large-capacity energy storage systems (ESS).

[0008] These batteries can have their status and operation managed and controlled by a battery management system (BMS). The BMS can be included with the batteries in a single device.

[0009] Additionally, the battery management system can manage and control the battery while being separated from the device containing the battery. For example, the battery management system can be implemented as a separate server device. In this case, the battery management system can collect battery data and vehicle data from vehicles and other devices, and utilize the collected data to manage and control the battery.

[0010] If a short circuit or other type of failure occurs within a battery, the risk of damage to devices containing the battery (e.g., EVs, ESS) may increase. Therefore, a method is needed to detect abnormal battery conditions and reduce the risk of damage to devices containing the battery.

[0011] Traditionally, battery cell diagnosis was performed using a calculation method that combined information such as State of Charge (SOC), current, capacity, and Open Circuit Voltage (OCV). Because this diagnostic method required numerous factors, it was difficult to diagnose if certain pieces of information were missing. This could be problematic for battery management systems implemented as server devices that collect data from vehicles, and could result in excessively high memory usage. Consequently, there is a pressing need to streamline the data required for battery diagnosis.

[0012] The embodiments disclosed in this document can provide a battery diagnostic device and an operating method thereof that can diagnose a battery abnormality using only battery OCV data information.

[0013] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0014] According to an embodiment disclosed in the present document, a battery diagnosis device may include an acquisition unit that acquires OCV (Open Circuit Voltage) data of a plurality of battery units, a deviation calculation unit that calculates an OCV change value of the plurality of battery units in a specified time period based on the OCV data, and calculates an OCV deviation value from an average representing a difference value between an average OCV change value of the plurality of battery units and an OCV change value of a target battery unit among the plurality of battery units, an EMA calculation unit that calculates an EMA deviation value by applying an EMA (Exponential Moving Average) filter to the OCV deviation value from the average of the target battery unit, and a diagnosis unit that diagnoses an abnormality of the target battery unit based on the EMA deviation value.

[0015] In a battery diagnostic device according to an embodiment disclosed in this document, the specified time period may be a time period between a first time point before charging of the battery unit is performed and a second time point after the charging is performed.

[0016] In a battery diagnostic device according to an embodiment disclosed in this document, the EMA filter can calculate the EMA deviation value by inputting the EMA deviation value of the previous cycle and the OCV deviation value compared to the average of the target battery unit into a specified mathematical formula.

[0017] In a battery diagnostic device according to an embodiment disclosed in this document, the specified mathematical expression may be the following mathematical expression 1.

[0018] [Mathematical Formula 1]

[0019] EMA t =α·dOCV t +(1-α)·EMA t-1

[0020] (In Equation 1, EMA t is the EMA deviation value, α is the weight, and dOCV t is the OCV deviation value compared to the average of the target battery unit, EMA t-1 is the EMA deviation value of the previous cycle.)

[0021] In a battery diagnostic device according to an embodiment disclosed in this document, the acquisition unit can acquire time series voltage data and time series current data of the plurality of battery units, and acquire the OCV data of a specified current value and a specified voltage range based on the time series voltage data and the time series current data.

[0022] In a battery diagnostic device according to an embodiment disclosed in this document, the battery unit may include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

[0023] A battery diagnostic device according to an embodiment disclosed in this document further includes an abnormality processing unit that performs an abnormality processing function based on an abnormality diagnosis result of each of the plurality of battery units, and the abnormality processing function may include a notification function or a short circuit function.

[0024] A battery diagnosis method according to an embodiment disclosed in the present document may include an operation of acquiring OCV (Open Circuit Voltage) data of a plurality of battery units, an operation of calculating an OCV change value of the plurality of battery units in a specified time period based on the OCV data, an operation of calculating an OCV deviation value from an average representing a difference value between an average OCV change value of the plurality of battery units and an OCV change value of a target battery unit among the plurality of battery units, an operation of calculating an EMA (Exponential Moving Average) deviation value by applying an EMA filter to the OCV deviation value from the average of the target battery unit, and an operation of diagnosing an abnormality of the target battery unit based on the EMA deviation value.

[0025] In a battery diagnosis method according to an embodiment disclosed in this document, the specified time period may be a time period between a first time point before charging is performed on the battery unit and a second time point after the charging is performed.

[0026] In a battery diagnosis method according to an embodiment disclosed in this document, the EMA filter can calculate the EMA deviation value by inputting the EMA deviation value of the previous cycle and the OCV deviation value compared to the average of the target battery unit into a specified mathematical formula.

[0027] In a battery diagnosis method according to an embodiment disclosed in this document, the specified mathematical expression may be the above mathematical expression 1.

[0028] A battery diagnosis method according to an embodiment disclosed in this document further includes an operation of acquiring time series voltage data and time series current data of the plurality of battery units, and the operation of acquiring the OCV data may include an operation of acquiring the OCV data of a specified current value and a specified voltage range based on the time series voltage data and the time series current data.

[0029] In a battery diagnosis method according to an embodiment disclosed in this document, the battery unit may include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

[0030] A battery diagnosis method according to an embodiment disclosed in this document further includes an operation of performing an abnormality processing function based on an abnormality diagnosis result of each of the plurality of battery units, and the abnormality processing function may include a notification function or a short circuit function.

[0031] According to the embodiments disclosed in this document, data used for battery abnormality diagnosis can be simplified.

[0032] According to the embodiments disclosed in this document, the accuracy of battery low capacity abnormality diagnosis can be improved.

[0033] In addition, various effects may be provided, either directly or indirectly, through this document.

[0034] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0035] FIG. 2 is a graph showing the OCV deviation value compared to the average of a battery produced by a battery diagnostic device according to one embodiment.

[0036] FIG. 3 is a graph showing the EMA deviation value of a battery calculated by a battery diagnostic device according to one embodiment.

[0037] Figure 4 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment.

[0038] Hereinafter, various embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0039] The various embodiments and terms used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0040] In this document, the phrases "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 each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0041] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0042] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0043] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0044] The battery diagnostic device (101) described below can be implemented as a BMS (Battery Management System) in an electronic device (102), but can also be implemented as various external devices such as a server, cloud, charger, or charger / discharger.

[0045] Referring to FIG. 1, a battery diagnostic device (101) can be connected to an electronic device (102) and a user terminal (104) via wires and / or wirelessly.

[0046] According to one embodiment, the connection (103) between the battery diagnostic device (101) and the electronic device (102) may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on a local area network (LAN) communication or a power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, wireless fidelity (WiFi), or infrared data association (IrDA)) or a wide-range communication network (cellular network, 4G network, 5G network).

[0047] According to another embodiment, the connection (103) between the battery diagnostic device (101) and the electronic device (102) may be a connection via a device-to-device communication method (e.g., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0048] According to one embodiment, the electronic device (102) may be a mobile device (e.g., a mobile phone, a laptop computer, a smart phone, a smart pad), an electric vehicle (e.g., an electric vehicle (EV), a hybrid EV (HEV), a plug-in HEV (PHEV), a fuel cell EV (FCEV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0049] According to one embodiment, the electronic device (102) may include a plurality of battery units (151, 153, 155). According to one embodiment, the plurality of battery units (151, 153, 155) may include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

[0050] According to one embodiment, the connection (105) between the battery diagnostic device (101) and the user terminal (104) may be a communication connection via a wired and / or wireless network.

[0051] According to one embodiment, the user terminal (104) may be a mobile device (e.g., a mobile phone, a laptop computer, a smart phone, a smart pad), or a personal computer (PC). According to one embodiment, the battery diagnostic device (101) may provide information related to the diagnostic results of the battery unit (151, 153, or 155) to the user terminal (104).

[0052] According to one embodiment, the battery diagnosis device (101) may include a communication circuit (110), a sensor (120), a memory (130), and a processor (140). Depending on the embodiment, the battery diagnosis device (101) illustrated in FIG. 1 may further include at least one component (e.g., a display, an input device, or an output device) other than the components illustrated in FIG. 1, or may omit at least one component (e.g., a sensor (120)) among the components illustrated in FIG. 1. For example, when the battery diagnosis device (101) is implemented as an external electronic device separate from the electronic device (102), such as a server or a cloud, the battery diagnosis device (101) may obtain status information of a plurality of battery units (151, 153, 155) using the communication circuit (110). In this case, the battery diagnosis device (101) may not include the sensor (120).

[0053] According to one embodiment, the communication circuit (110) can establish a wired communication channel and / or a wireless communication channel between the battery diagnostic device (101) and the electronic device (102) and / or the user terminal (104), and transmit and receive data with the electronic device (102) and / or the user terminal (104) through the established communication channel.

[0054] According to one embodiment, the sensor (120) can measure information (e.g., voltage, current, temperature, etc.) related to the status of a plurality of battery units (121, 122, 123) of the electronic device (102). For example, when the battery diagnosis device (101) is implemented as a BMS within the electronic device, the battery diagnosis device (101) can directly measure the status values ​​of the plurality of battery units (151, 153, 155) using the sensor (120).

[0055] According to one embodiment, the communication circuit (110) and / or the sensor (120) may obtain time series data related to the states of the plurality of battery units (151, 153, 155). In one embodiment, the time series data related to the states of the plurality of battery units (151, 153, 155) may be data representing voltage, current, resistance, state of charge (SOC), state of health (SOH), and / or temperature of the plurality of battery units (151, 153, 155) over time.

[0056] According to one embodiment, the memory (130) may include volatile memory and / or non-volatile memory.

[0057] According to one embodiment, the memory (130) may store data used by at least one component (e.g., processor (140)) of the battery diagnosis device (101). For example, the data may include software (or instructions related thereto), input data, or output data. In one embodiment, the instructions, when executed by the processor (140), may cause the battery abnormality diagnosis device (101) to perform operations defined by the instructions.

[0058] According to one embodiment, the memory (140) may include one or more software (e.g., an acquisition unit (131), a deviation calculation unit (132), an EMA (Exponential Moving Average) calculation unit (133), a diagnosis unit (134), and an anomaly processing unit (135)).

[0059] According to one embodiment, the processor (140) may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0060] According to one embodiment, the processor (140) may execute software stored in the memory (130) (e.g., acquisition unit (131), deviation calculation unit (132), EMA calculation unit (133), diagnosis unit (134), and abnormality processing unit (135)) to control at least one other component (e.g., hardware or software component) of the battery diagnosis device (101) connected to the processor (140) and perform various data processing or operations.

[0061] Hereinafter, with reference to FIGS. 2 and 3, a method for diagnosing anomalies in a plurality of battery units (151, 153, 155) through an acquisition unit (131), a deviation calculation unit (132), an EMA calculation unit (133), a diagnosis unit (134), and anomaly processing unit (135) by a battery diagnosis device (101) will be described.

[0062] Fig. 2 is a graph showing an OCV deviation value compared to the average of a battery calculated by a battery diagnostic device according to one embodiment. Fig. 3 is a graph showing an EMA deviation value of a battery calculated by a battery diagnostic device according to one embodiment.

[0063] According to one embodiment, the acquisition unit (131) can acquire OCV (Open Circuit Voltage) data of a plurality of battery units (151, 153, 155). According to one embodiment, the acquisition unit (131) can acquire the OCV data using a communication circuit (110) and / or a sensor (120).

[0064] According to one embodiment, the acquisition unit (131) can acquire time series voltage data and time series current data of a plurality of battery units (151, 153, 155). In this case, the acquisition unit (131) can acquire the OCV data based on the time series voltage data and the time series current data. For example, the acquisition unit (131) can acquire only data that satisfies a specified current value (e.g., 0 A) and a specified voltage range (e.g., a voltage range of 3.5 V or more and less than 4.3 V) in the time series voltage data and the time series current data as the OCV data.

[0065] According to one embodiment, the deviation calculation unit (132) can calculate an OCV deviation value compared to the average of the target battery unit (151, 153, or 155) based on the OCV data acquired by the acquisition unit (131).

[0066] According to one embodiment, the deviation calculation unit (132) can extract OCV data in a specified voltage range (e.g., 0.4 V or higher) from the OCV data. The deviation calculation unit (132) can calculate an OCV deviation value compared to the average based on the extracted OCV data in the specified voltage range.

[0067] According to one embodiment, the deviation calculation unit (132) may calculate an OCV change value of a plurality of battery units (151, 153, 155) at a specified time period based on OCV data. According to one embodiment, the specified time period may be set as a time period between a first time period before charging is performed on the battery unit (151, 153, or 155) and a second time period after the charging is performed. For example, the deviation calculation unit (132) may calculate an OCV change value, which is a difference value between an OCV value at the first time period and an OCV value at the second time period, based on the OCV data of the battery unit (151, 153, or 155).

[0068] According to one embodiment, the deviation calculation unit (132) may calculate an OCV deviation value of the target battery unit (151, 153, or 155) relative to the average based on the OCV change values ​​of the plurality of battery units (151, 153, 155). According to one embodiment, the deviation calculation unit (132) may calculate a difference value (e.g., difference value = average OCV change value - OCV change value of the target battery unit) between the average OCV change value of the plurality of battery units (151, 153, 155) and the OCV change value of the target battery unit (151, 153, or 155).

[0069] Referring to FIG. 2, a graph (200) showing the OCV deviation values ​​compared to the average over time of the battery units to be diagnosed can be confirmed. In the graph (200), the X-axis can represent time, and the Y-axis can represent the OCV deviation compared to the average (the difference between the average OCV change value of the battery units to be diagnosed and the OCV change value of each battery unit).

[0070] According to one embodiment, the deviation calculation unit (132) can calculate the OCV deviation value compared to the average of the battery units to be diagnosed at each specified period. The battery diagnosis device (101) can manage OCV deviation data such as the graph (200) by accumulating the OCV deviation value compared to the average calculated by the deviation calculation unit (132) in the memory (130) for a specified period of time.

[0071] Referring back to FIG. 1, the EMA calculation unit (133) can calculate an EMA deviation value by applying an EMA filter to the average-to-OCV deviation value of the target battery unit (151, 153, or 155) calculated by the deviation calculation unit (132).

[0072] According to one embodiment, the EMA filter may input the EMA deviation value of the previous cycle of the target battery unit (151, 153, or 155) and the OCV deviation value compared to the average of the current cycle into a specified mathematical formula to produce the EMA deviation value of the current cycle. For example, the specified mathematical formula may be the following mathematical formula 1.

[0073] [Formula 1]

[0074] EMA t =α·dOCV t +(1-α)·EMA t-1

[0075] In the above mathematical expression 1, EMA t is the EMA deviation value of the current cycle of the target battery unit (151, 153, or 155), α is the weight, dOCV t is the OCV deviation value compared to the average of the current cycle of the target battery unit (151, 153, or 155), EMA t-1 is the EMA deviation value of the previous cycle of the target battery unit (151, 153, or 155). Here, the weight may be set in various ways according to the specifications of the plurality of battery units (151, 153, 155). For example, the weight may be set to 0.05, but is not limited thereto.

[0076] Referring to FIG. 3, a graph (300) showing the EMA deviation values ​​of the battery units to be diagnosed in the current cycle can be confirmed. In the graph (300), the X-axis can represent the battery unit number, and the Y-axis can represent the EMA deviation value.

[0077] Referring back to FIG. 1, the diagnostic unit (134) can diagnose an abnormality in the target battery unit (151, 153, or 155) based on the EMA deviation value calculated by the EMA deviation calculation unit (133). For example, the diagnostic unit (134) can diagnose whether the target battery unit (151, 153, or 155) has a low capacity.

[0078] According to one embodiment, the diagnostic unit (134) can diagnose an abnormality in the target battery unit (151, 153, or 155) by comparing the EMA deviation value with a threshold value pre-stored in the memory (130).

[0079] According to one embodiment, the diagnostic unit (134) can diagnose an abnormality in the plurality of battery units (151, 153, 155) based on the EMA deviation value of each of the plurality of battery units (151, 153, 155).

[0080] According to one embodiment, the abnormality processing unit (135) may perform an abnormality processing function based on the abnormality diagnosis results of a plurality of battery units (151, 153, 155). Here, the abnormality processing function may include a notification function or a short circuit function.

[0081] According to one embodiment, the abnormality processing unit (135) can transmit the abnormality diagnosis results of the plurality of battery units (151, 153, 155) to a user terminal (104) connected via a wired and / or wireless network.

[0082] According to one embodiment, the abnormality processing unit (135) may isolate an abnormal battery unit from the electronic device (102) based on the abnormality diagnosis results of the plurality of battery units (151, 153, 155). Here, the isolation may include electrical and / or mechanical isolation.

[0083] Fig. 4 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment. Fig. 4 can be explained using the configurations of Fig. 1.

[0084] The embodiment illustrated in FIG. 4 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 4, and some of the steps illustrated in FIG. 4 may be omitted, the order between steps may be changed, or steps may be merged.

[0085] Referring to FIG. 4, in operation 405, the battery diagnostic device (101) can obtain OCV data of a plurality of battery units (151, 153, 155). According to one embodiment, the battery diagnostic device (101) can obtain the OCV data using a communication circuit (110) and / or a sensor (120).

[0086] According to one embodiment, the battery diagnosis device (101) can obtain time series voltage data and time series current data of a plurality of battery units (151, 153, 155). In this case, the battery diagnosis device (101) can obtain the OCV data based on the time series voltage data and the time series current data. For example, the battery diagnosis device (101) can obtain only data that satisfies a specified current value (e.g., 0 A) and a specified voltage range (e.g., a voltage range of 3.5 V or more and less than 4.3 V) in the time series voltage data and the time series current data as the OCV data.

[0087] In operation 410, the battery diagnostic device (101) can calculate an OCV change value of a plurality of battery units (151, 153, 155) at a specified time period based on the OCV data acquired in operation 405. According to one embodiment, the specified time period may be set as a time period between a first time period before charging is performed on the battery unit (151, 153, or 155) and a second time period after the charging is performed. For example, the deviation calculation unit (132) can calculate an OCV change value, which is a difference value between an OCV value at the first time period and an OCV value at the second time period, based on the OCV data of the battery unit (151, 153, or 155).

[0088] According to one embodiment, the battery diagnostic device (101) can extract OCV data in a specified voltage range (e.g., 0.4 V or higher) from the OCV data. The battery diagnostic device (101) can calculate the OCV change value based on the extracted OCV data in the specified voltage range.

[0089] In operation 415, the battery diagnostic device (101) can calculate an OCV deviation value compared to the average of the target battery unit (151, 153, or 155) based on the OCV change values ​​of the plurality of battery units (151, 153, 155) calculated in operation 410.

[0090] According to one embodiment, the battery diagnostic device (101) can calculate a difference value (e.g., difference value = average OCV change value - OCV change value of target battery unit) between an average OCV change value of a plurality of battery units (151, 153, 155) and an OCV change value of a target battery unit (151, 153, or 155) as an OCV deviation value compared to the average.

[0091] In operation 420, the battery diagnostic device (101) may calculate an EMA deviation value of the target battery unit (151, 153, or 155). According to one embodiment, the battery diagnostic device (101) may calculate the EMA deviation value by applying an EMA filter to the average-to-OCV deviation value calculated in operation 415.

[0092] According to one embodiment, the EMA filter may input the EMA deviation value of the previous cycle of the target battery unit (151, 153, or 155) and the OCV deviation value compared to the average of the current cycle into a designated mathematical formula to produce the EMA deviation value of the current cycle. For example, the designated mathematical formula may be the above mathematical formula 1.

[0093] In operation 425, the battery diagnostic device (101) can diagnose an abnormality in the target battery unit (151, 153, or 155) based on the EMA deviation value calculated in operation 420. For example, the battery diagnostic device (101) can diagnose whether the target battery unit (151, 153, or 155) has a low capacity.

[0094] According to one embodiment, the battery diagnostic device (101) can diagnose an abnormality in a target battery unit (151, 153, or 155) by comparing the EMA deviation value with a pre-stored threshold value.

[0095] According to one embodiment, the battery diagnostic device (101) can diagnose an abnormality in a plurality of battery units (151, 153, 155) based on an EMA deviation value of each of the plurality of battery units (151, 153, 155).

[0096] According to one embodiment, the battery diagnostic device (101) may perform an abnormality processing function based on abnormality diagnosis results of a plurality of battery units (151, 153, 155). Here, the abnormality processing function may include a notification function or a short circuit function.

[0097] According to one embodiment, the battery diagnostic device (101) can transmit abnormal diagnosis results of the plurality of battery units (151, 153, 155) to a user terminal (104) connected via a wired and / or wireless network.

[0098] According to one embodiment, the battery diagnostic device (101) can isolate a faulty battery unit from the electronic device (102) based on the abnormal diagnosis results of a plurality of battery units (151, 153, 155). Here, the isolation can include electrical and / or mechanical isolation.

[0099] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

Claims

1. An acquisition unit that acquires OCV (Open Circuit Voltage) data of multiple battery units; A deviation calculation unit that calculates OCV change values ​​of the plurality of battery units at a specified time interval based on the OCV data, and calculates an OCV deviation value compared to an average indicating a difference value between an average OCV change value of the plurality of battery units and an OCV change value of a target battery unit among the plurality of battery units; An EMA calculation unit that calculates an EMA deviation value by applying an EMA (Exponential Moving Average) filter to the OCV deviation value compared to the average of the target battery unit; and A battery diagnostic device comprising a diagnostic unit that diagnoses an abnormality of the target battery unit based on the EMA deviation value.

2. In claim 1, A battery diagnostic device, wherein the above-mentioned specified time period is a time period between a first time point before charging is performed on the battery unit and a second time point after the charging is performed.

3. In claim 1, A battery diagnostic device wherein the EMA filter calculates the EMA deviation value by inputting the EMA deviation value of the previous cycle and the OCV deviation value compared to the average of the target battery unit into a specified mathematical formula.

4. In claim 3, The above-mentioned mathematical expression is the following mathematical expression 1, a battery diagnostic device. [Mathematical Formula 1] EMA t =α·dOCV t +(1-α)·EMA t-1 (In mathematical expression 1, EMAt is the EMA deviation value, α is a weight, dOCVt is the OCV deviation value compared to the average of the target battery unit, and EMAt-1 is the EMA deviation value of the previous cycle.) 5. In claim 1, The above acquisition part is, Obtain time series voltage data and time series current data of the above plurality of battery units, A battery diagnostic device that obtains the OCV data of a specified current value and a specified voltage range based on the time series voltage data and the time series current data.

6. In claim 1, A battery diagnostic device, wherein the battery unit comprises at least one of a battery cell, a battery module, a battery pack, or a battery rack.

7. In claim 1, Further comprising an abnormality processing unit that performs an abnormality processing function based on the abnormality diagnosis results of each of the plurality of battery units, A battery diagnostic device, wherein the above abnormality handling function includes a notification function or a short circuit function.

8. Operation of acquiring OCV (Open Circuit Voltage) data of multiple battery units; An operation of calculating an OCV change value of the plurality of battery units in a specified time period based on the OCV data; An operation of calculating an average-to-OCV deviation value representing a difference value between an average OCV change value of the plurality of battery units and an OCV change value of a target battery unit among the plurality of battery units; An operation of calculating an EMA (Exponential Moving Average) deviation value by applying an EMA filter to the OCV deviation value compared to the average of the target battery unit; and A battery diagnosis method, comprising an operation of diagnosing an abnormality of the target battery unit based on the EMA deviation value.

9. In claim 8, A battery diagnosis method, wherein the above-mentioned specified time period is a time period between a first time point before charging is performed on the battery unit and a second time point after the charging is performed.

10. In claim 8, A battery diagnosis method, wherein the EMA filter calculates the EMA deviation value by inputting the EMA deviation value of the previous cycle and the OCV deviation value compared to the average of the target battery unit into a specified mathematical formula.

11. In claim 10, The above-mentioned mathematical expression is the following mathematical expression 1, a battery diagnosis method. [Mathematical Formula 1] EMA t =α·dOCV t +(1-α)·EMA t-1 (In mathematical expression 1, EMAt is the EMA deviation value, α is a weight, dOCVt is the OCV deviation value compared to the average of the target battery unit, and EMAt-1 is the EMA deviation value of the previous cycle.) 12. In claim 8, Further comprising an operation of acquiring time series voltage data and time series current data of the plurality of battery units, A battery diagnosis method, wherein the operation of acquiring the OCV data includes an operation of acquiring the OCV data of a specified current value and a specified voltage range based on the time series voltage data and the time series current data.

13. In claim 8, A battery diagnostic method, wherein the battery unit comprises at least one of a battery cell, a battery module, a battery pack, or a battery rack.

14. In claim 8, Further comprising an operation of performing an abnormality processing function based on the abnormality diagnosis results of each of the plurality of battery units, A battery diagnostic method, wherein the above abnormality handling function includes a notification function or a short circuit function.

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