Electronic device and battery diagnosis method thereof

Abstract

An electronic device according to an embodiment of the present disclosure may comprise: an information acquisition interface; and a processor operatively connected to the information acquisition interface, wherein the processor: acquires time-series temperature data of a battery by using the information acquisition interface; calculates a window size for calculating a moving average temperature of the battery on the basis of the time-series temperature data; calculates the moving average temperature of the battery on the basis of the calculated window size; and diagnoses an abnormality of the battery on the basis of the calculated moving average temperature.

Description

Electronic device and method for diagnosing its battery

[0001] This application claims the benefit of priority to Republic of Korea Patent Application No. 10-2024-0104470, filed August 6, 2024, the entire contents of which are incorporated herein by reference.

[0002] Embodiments disclosed in this document relate to an electronic device and a battery diagnosis method thereof.

[0003] As the applications of these secondary batteries expand, the importance of management systems for more efficient use and management of these batteries is increasing. The status and operation of secondary batteries can be managed and controlled by a battery management system (BMS). The BMS can be integrated with the battery within a single device.

[0004] 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.

[0005] Meanwhile, if a battery is defective, 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.

[0006] Traditionally, methods have been used to diagnose battery abnormalities using the temperature difference between the battery's periphery and surface. However, this diagnostic method suffers from the unreliability of temperature difference values ​​based on the ambient temperature, leading to misdiagnosis and overdiagnosis. Furthermore, conventional moving average algorithms use a fixed number of data points for the moving average, making it difficult to detect abnormal batteries by quickly reflecting rapid changes in data.

[0007] According to one embodiment of the present disclosure, an electronic device capable of diagnosing an abnormality of a battery by adjusting a window size for calculating a moving average according to a change in real-time temperature data of the battery and a battery diagnosis method thereof can be provided.

[0008] The technical problems to be solved by the embodiments of the present disclosure are not limited to the technical problems described above, and other technical problems can be inferred from the following embodiments.

[0009] An electronic device according to one embodiment of the present disclosure includes an information acquisition interface, and a processor operatively connected to the information acquisition interface, wherein the processor acquires time-series temperature data of a battery using the information acquisition interface, calculates a window size for calculating a moving average temperature of the battery based on the time-series temperature data, calculates the moving average temperature of the battery based on the calculated window size, and diagnoses an abnormality of the battery based on the calculated moving average temperature.

[0010] In an electronic device according to one embodiment of the present disclosure, the time series temperature data may continuously represent the temperature of the battery over time, and the window size may represent the time length of a time interval based on a diagnosis target time point.

[0011] In an electronic device according to one embodiment of the present disclosure, the time series temperature data may discretely represent the temperature of the battery over time according to a temperature measurement cycle, and the window size may represent the number of temperatures of the battery over time.

[0012] In an electronic device according to one embodiment of the present disclosure, the processor may calculate a slope between a first temperature and a second temperature of the time series temperature data, and calculate the window size based on the calculated slope, wherein the first temperature may be a temperature of the battery at a first point in time, and the second temperature may be a temperature of the battery at a second point in time corresponding to a diagnosis target point in time after a specified time from the first point in time.

[0013] In an electronic device according to one embodiment of the present disclosure, the processor may calculate the window size such that the calculated slope and the window size are inversely proportional.

[0014] In an electronic device according to one embodiment of the present disclosure, the processor can calculate the window size based on the following mathematical expression 1.

[0015] [Mathematical Formula 1]

[0016]

[0017] (In Equation 1, N p is the above window size, N s is the preset initial window size, S is the calculated slope.)

[0018] In an electronic device according to one embodiment of the present disclosure, the processor can diagnose an abnormality in the battery by comparing the temperature of the battery at the time of diagnosis with the calculated moving average temperature.

[0019] In an electronic device according to one embodiment of the present disclosure, the processor can diagnose the battery as an abnormal battery if the temperature of the battery at the diagnosis target point in time is higher than the calculated moving average temperature.

[0020] A battery diagnosis method performed by an electronic device according to one embodiment of the present disclosure may include an operation of acquiring time series temperature data of a battery, an operation of calculating a window size for calculating a moving average temperature of the battery based on the time series temperature data, an operation of calculating the moving average temperature of the battery based on the calculated window size, and an operation of diagnosing an abnormality of the battery based on the calculated moving average temperature.

[0021] In a battery diagnosis method performed by an electronic device according to an embodiment of the present disclosure, the time series temperature data may continuously represent the temperature of the battery over time, and the window size may represent the time length of a time interval based on a diagnosis target time point.

[0022] In a battery diagnosis method performed by an electronic device according to an embodiment of the present disclosure, the time series temperature data may discretely represent the temperature of the battery according to a temperature measurement cycle over time, and the window size may represent the number of temperatures of the battery over time.

[0023] In a battery diagnosis method performed by an electronic device according to one embodiment of the present disclosure, the operation of calculating the window size includes an operation of calculating a slope between a first temperature and a second temperature of the time-series temperature data, and an operation of calculating the window size based on the calculated slope, wherein the first temperature may be a temperature of the battery at a first point in time, and the second temperature may be a temperature of the battery at a second point in time corresponding to a diagnosis target point in time after a specified time from the first point in time.

[0024] In a battery diagnosis method performed by an electronic device according to an embodiment of the present disclosure, the operation of calculating the window size may include an operation of calculating the window size such that the calculated slope and the window size are inversely proportional.

[0025] In a battery diagnosis method performed by an electronic device according to an embodiment of the present disclosure, the operation of calculating the window size may be based on the mathematical expression 1 above.

[0026] In a battery diagnosis method performed by an electronic device according to one embodiment of the present disclosure, the operation of diagnosing an abnormality of the battery may include an operation of diagnosing the battery as an abnormal battery if the temperature of the battery at the diagnosis target point in time is higher than the calculated moving average temperature.

[0027] According to the embodiments disclosed in this document, by adjusting the window size for calculating the moving average according to changes in the real-time temperature data of the battery, it is possible to diagnose the battery while responding to rapid data changes.

[0028] The effects of the invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0029] FIG. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure.

[0030] FIG. 2 is a diagram illustrating an example in which an electronic device according to one embodiment of the present disclosure calculates a moving average temperature of a battery based on time-series temperature data that continuously represents the temperature of the battery over time.

[0031] FIG. 3 is a diagram illustrating an example in which an electronic device according to an embodiment of the present disclosure calculates a moving average temperature of a battery based on time-series temperature data that discretely represents the temperature of the battery over time according to a temperature measurement cycle.

[0032] FIG. 4 is a flowchart of the operation of an electronic device according to an embodiment of the present disclosure.

[0033] In describing the embodiments, descriptions of technical details that are well known in the technical field to which the present disclosure pertains and are not directly related to the present disclosure will be omitted. This is to convey the gist of the present disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0034] For the same reason, some components in the attached drawings are exaggerated, omitted, or schematically depicted. Furthermore, the dimensions of each component do not entirely reflect its actual size. Identical or corresponding components in each drawing are assigned the same reference numbers.

[0035] The advantages and features of the present disclosure, and methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided solely to ensure that the disclosure of the present disclosure is complete and to fully inform those skilled in the art of the scope of the invention, and the present disclosure is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

[0036] At this time, it will be understood that each block of the processing flowchart drawings and combinations of the flowchart drawings can be performed by computer program instructions. These computer program instructions can be loaded into a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flowchart block(s). These computer program instructions can also be stored in a computer-available or computer-readable memory that can be directed to a computer or other programmable data processing equipment to implement the functions in a specific manner, so that the instructions stored in the computer-available or computer-readable memory can produce an article of manufacture that includes a command means for performing the functions described in the flowchart block(s). The computer program instructions can also be loaded onto a computer or other programmable data processing equipment, so that a series of operation steps are performed on the computer or other programmable data processing equipment to create a computer-executable process, so that the instructions that execute the computer or other programmable data processing equipment can provide steps for performing the functions described in the flowchart block(s).

[0037] Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a specific logical function(s). It should also be noted that in some alternative implementation examples, the functions described in the blocks may occur out of order. For example, two blocks depicted in succession may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on their respective functions.

[0038] Here, the term '~ unit' used in the present embodiment means a software or hardware component such as an FPGA or ASIC, and the '~ unit' performs certain roles. However, the '~ unit' is not limited to software or hardware. The '~ unit' may be configured to be on an addressable storage medium and may be configured to regenerate one or more processors. Therefore, for example, the '~ unit' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'. In addition, the components and '~ units' may be implemented to regenerate one or more CPUs within a device or a secure multimedia card.

[0039] The expression “at least one of a, b and c” described throughout the specification may encompass ‘a alone’, ‘b alone’, ‘c alone’, ‘a and b’, ‘a and c’, ‘b and c’, or ‘all of a, b and c’.

[0040] The "terminal" mentioned below may be implemented as a computer or portable terminal that can connect to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, laptop, etc. equipped with a web browser, and the portable terminal may include, for example, a wireless communication device that guarantees portability and mobility, and may include all types of handheld-based wireless communication devices such as communication-based terminals such as IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution), smartphones, tablet PCs, etc.

[0041] Below, embodiments of the present disclosure are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein.

[0042] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0043] FIG. 1 is a block diagram of an electronic device (100) according to one embodiment of the present disclosure.

[0044] The electronic device (100) described below may be implemented as a BMS (Battery Management System), OBD (On-Board Diagnostics), or ECU (Electronic Control Unit) placed in a vehicle, and may also be implemented as a notebook, desktop, laptop, or server computing device that receives and processes battery data from an external electronic device (e.g., a vehicle).

[0045] Referring to FIG. 1, the electronic device (100) may include an information acquisition interface (110), a memory (120), and a processor (130). According to an embodiment, the electronic device (100) 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.

[0046] According to one embodiment, the information acquisition interface (110) can acquire time-series temperature data of a battery. The battery referred to below may be a battery cell, a battery module, or a battery pack disposed in a vehicle.

[0047] For example, the information acquisition interface (110) may be implemented as a sensor that measures information related to the state of the battery (e.g., voltage, current, temperature, etc.). In this case, the information acquisition interface (110) may acquire time-series temperature data by measuring the temperature of the battery.

[0048] As another example, the information acquisition interface (110) may be implemented as a communication circuit that establishes a wired communication channel and / or a wireless communication channel between the electronic device (100) and an external electronic device (e.g., a vehicle) and transmits and receives data with the external electronic device through the established communication channel. In this case, the information acquisition interface (110) may receive time-series temperature data of the battery from the external electronic device.

[0049] Here, communication, i.e., transmission and reception of data, can be performed wired or wirelessly. To this end, the information acquisition interface (110) may include a wired communication module that communicates with components within the electronic device (100) via a CAN (Controller Area Network) or connects to the Internet, etc. via a LAN (Local Area Network), a mobile communication module that connects to a mobile communication network via a mobile communication base station to transmit and receive data, a short-range communication module that uses a WLAN (Wireless Local Area Network) series communication method such as Wi-Fi or a WPAN (Wireless Personal Area Network) series communication method such as Bluetooth or Zigbee, a satellite communication module that uses a GNSS (Global Navigation Satellite System) such as a GPS (Global Positioning System), or a combination thereof.

[0050] According to one embodiment, the time series temperature data may be time series continuous data (e.g., data (210) of FIG. 2A) that continuously represents the temperature of the battery over time, or time series discrete data (e.g., data (220) of FIG. 2B) that discretely represents the temperature of the battery over time according to a temperature measurement cycle. The electronic device (100) may calculate a window size and a moving average temperature based on the time series continuous data or the time series discrete data, and the contents related to the time series continuous data may be described through FIGS. 2A and 3A, which will be described later, and the contents related to the time series discrete data may be described through FIGS. 2B and 3B, which will be described later.

[0051] According to one embodiment, the memory (120) may include volatile memory and / or non-volatile memory.

[0052] According to one embodiment, the memory (120) may store data used by at least one component (e.g., the processor (130)) of the electronic device (100). 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 (130), may cause the electronic device (100) to perform operations defined by the instructions.

[0053] According to one embodiment, the processor (130) may be implemented as a computer or similar device according to hardware, software, or a combination thereof. In terms of hardware, the processor (130) may be implemented in the form of an electronic circuit that processes electrical signals to perform a control function, and in terms of software, the processor (130) may be implemented in the form of a program that drives the hardware processor (130). According to one embodiment, the processor (130) may be operatively connected to components included in the electronic device (100) (e.g., information acquisition interface (110) and / or memory (120)) and control the connected components.

[0054] According to one embodiment, the processor (130) 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.

[0055] Meanwhile, unless otherwise specifically mentioned in the following description, the operation of the electronic device (100) may be interpreted as being performed under the control of the processor (130).

[0056] Hereinafter, a method for an electronic device (100) to diagnose an abnormality in a battery will be described with reference to FIGS. 2 and 3.

[0057] FIG. 2 is a diagram illustrating an example in which an electronic device (100) according to one embodiment of the present disclosure calculates a moving average temperature of a battery based on time series temperature data (200) that continuously indicates the temperature of the battery over time.

[0058] FIG. 3 is a diagram illustrating an example in which an electronic device (100) according to one embodiment of the present disclosure calculates a moving average temperature of a battery based on time series temperature data (300) that discretely represents the temperature of the battery over time according to a temperature measurement cycle.

[0059] Referring to FIGS. 2 and 3, the electronic device (100) can obtain time-series temperature data (200 or 300) of the battery. The time-series temperature data (200) of FIG. 2 can continuously represent the temperature of the battery over time, and the time-series temperature data (300) of FIG. 3 can discretely represent the temperature of the battery over time according to a temperature measurement cycle.

[0060] According to one embodiment, the electronic device (100) may calculate a window size for calculating a moving average temperature of a battery based on time series temperature data (200 or 300). Here, the window size may be expressed differently depending on the type of time series temperature data. For example, if the electronic device (100) calculates the window size based on the time series temperature data (200) of FIG. 2, the calculated window size may represent the time length of a time interval based on a diagnosis target time point. As another example, if the electronic device (100) calculates the window size based on the time series temperature data (300) of FIG. 3, the calculated window size may represent the number of temperatures of the battery over time.

[0061] According to one embodiment, the electronic device (100) can calculate the slope of the time series temperature data (200 or 300). According to one embodiment, the electronic device (100) can calculate the slope between two temperatures of the time series temperature data (200 or 300). For example, the electronic device (100) can calculate the slope between the battery temperature at a specific point in time of the time series temperature data (200 or 300) and the battery temperature at a diagnosis target point in time after a specific time from the specific point in time. Here, the specified time can be set in various ways depending on the specifications of the battery, and when the electronic device (100) calculates the window size based on the time series temperature data (300) of FIG. 3, the specified time can be set to an integer multiple of the temperature measurement cycle.

[0062] For example, when diagnosing a battery abnormality using the second time point (T2) of the time series temperature data (200) of FIG. 2 as the diagnosis target time point, the electronic device (100) determines the second temperature (D) at the second time point (T2). 1-2 ) and the first temperature (D) at the first time point (T1) before the specified time from the second time point (T2). 1-1 ) slope between (e.g. D 1-2 - D 1-1 / T2- T1) can be calculated. In addition, when diagnosing a battery abnormality using the third time point (T3) of the time series temperature data (200) of FIG. 2 as a diagnosis target time point, the electronic device (100) can calculate the third temperature (D) at the third time point (T3). 1-3 ) and the second temperature (D) at the second time point (T2) before the specified time from the third time point (T3). 1-2 ) slope between (e.g. D 1-3 - D 1-2 / T3- T2) can also be produced.

[0063] As another example, the 11th temperature measurement cycle (P) of the time series temperature data (300) of FIG. 3 11) as the diagnosis target point, the electronic device (100) is used to diagnose a battery abnormality, the 11th temperature measurement cycle (P 11 ) at temperature (D) 2-11 ) and the 11th temperature measurement cycle (P 11 ) is the temperature (D) at the first temperature measurement cycle (P1) prior to the specified cycle (e.g., 10 cycles) 2-1 ) slope between (e.g. D 2-11 - D 2-1 / 10) can be produced. In addition, the 24th temperature measurement cycle (P) of the time series temperature data (300) of FIG. 3 24 ) as the diagnosis target point, the electronic device (100) is used to diagnose a battery abnormality at the 24th temperature measurement cycle (P 24 ) at temperature (D) 2-24 ) and the 24th temperature measurement cycle (P 24 ) from the 14th temperature measurement cycle (P) prior to the specified cycle (e.g. 10 cycles) 14 ) at temperature (D) 2-14 ) slope between (e.g. D 2-24 - D 2-14 / 10) can also be produced.

[0064] According to one embodiment, the electronic device (100) can calculate a window size for calculating a moving average temperature of the battery based on the calculated slope.

[0065] According to one embodiment, the electronic device (100) can calculate the window size such that the slope between two temperatures of the time series temperature data (200 or 300) and the window size are inversely proportional.

[0066] For example, in the time series temperature data (200) of FIG. 2, the first temperature (D) at the first time point (T1) 1-1 ) and the second temperature (D) at the second time point (T2) 1-2 ) slope (e.g. D 1-2 - D 1-1 / T2- T1) is the second temperature (D) at the second time point (T2). 1-2) and the third temperature (D) at the third time point (T3) 1-3 ) slope between (e.g. D 1-3 - D 1-2 / T3- T2) may be smaller than the size of the window (W1) at the second time point (T2) produced (e.g., 35 seconds) may be larger than the size of the window (W2) at the third time point (T3) (e.g., 24 seconds).

[0067] As another example, in the time series temperature data (300) of FIG. 3, the temperature (D) at the first temperature measurement cycle (P1) 2-1 ) and the 11th temperature measurement cycle (P 11 ) at temperature (D) 2-11 ) slope between (e.g. D 2-11 - D 2-1 / 10) is the 14th temperature measurement cycle (P 14 ) at temperature (D) 2-14 ) and the 24th temperature measurement cycle (P 24 ) at temperature (D) 2-24 ) slope between (e.g. D 2-24 - D 2-14 / 10) may be less than. In this case, the 11th temperature measurement cycle (P) produced 11 ) the size of the window (W3) (e.g. 10) is the 24th temperature measurement cycle (P 24 ) may be larger than the size of the window (W4) (e.g. 4).

[0068] Accordingly, the electronic device (100) can improve the accuracy of diagnosis by diagnosing battery abnormalities through an adaptive moving average algorithm according to changes in battery temperature.

[0069] According to one embodiment, the electronic device (100) can calculate the window size at the diagnosis target time based on the following mathematical expression 1.

[0070]

[0071] In mathematical expression 1, N p is the window size at the time of diagnosis, N sis the preset initial window size, and S is the slope between the two temperatures calculated at the diagnosis target point.

[0072] For example, when the electronic device (100) calculates the window size based on the time series temperature data (200) of FIG. 2, the preset initial window size may be a positive time length and may be set in various ways depending on the specifications of the battery.

[0073] As another example, if the electronic device (100) calculates the window size based on the time series temperature data (300) of FIG. 3, the preset initial window size may be a natural number greater than or equal to 2, and may be set in various ways depending on the specifications of the battery. In addition, in this case, the electronic device (100) calculates the window size (N) at the diagnosis target time point calculated according to mathematical expression 1. p ) can be corrected to a natural number by rounding up, down, or up.

[0074] According to one embodiment, the electronic device (100) may calculate a moving average temperature of the battery based on a window size at a diagnosis target point in time. Here, the moving average may mean any one of a simple moving average (SMA), a weighted moving average (WMA), and an exponential moving average (EMA).

[0075] For example, the electronic device (100) can calculate the average value of temperature values ​​within a window (W1) based on the window size at a second time point (T2), which is a diagnostic target time point of the time series temperature data (200) of FIG. 2, as the moving average temperature of the battery.

[0076] As another example, the electronic device (100) is the 11th temperature measurement cycle (P) which is the diagnostic target point of the time series temperature data (300) of FIG. 3. 11Temperature values ​​(D) within the window (W3) based on the window size in 2-2 , D 2-3 , D 2-4 , D 2-5 , D 2-6 , D 2-7 , D 2-8 , D 2-9 , D 2-10 , D 2-11 ) can be calculated as the moving average temperature of the battery.

[0077] According to one embodiment, the electronic device (100) can diagnose a battery abnormality based on the calculated moving average temperature. The electronic device (100) can diagnose a battery abnormality by comparing the temperature of the battery at the time of diagnosis with the calculated moving average temperature. For example, if the temperature of the battery at the time of diagnosis is higher than the calculated moving average temperature, the electronic device (100) can diagnose the battery as abnormal.

[0078] FIG. 4 is a flowchart illustrating the operation of an electronic device according to an embodiment of the present disclosure. Since the operation method of FIG. 4 can be performed by the electronic device (100) of FIG. 1, any description overlapping with the above-described content may be omitted, and the method may be described using the components of FIG. 1.

[0079] The embodiment illustrated in FIG. 4 is only one embodiment, and the order of operations according to various embodiments of the present disclosure may be different from that illustrated in FIG. 4, and some operations illustrated in FIG. 4 may be omitted, the order between operations may be changed, or operations may be merged.

[0080] Referring to FIG. 4, in operation 410, the electronic device (100) may acquire time-series temperature data of the battery. Here, the time-series temperature data may be time-series continuous data that continuously represents the temperature of the battery over time, or time-series discrete data that discretely represents the temperature of the battery over time according to a temperature measurement cycle.

[0081] In operation 420, the electronic device (100) can calculate a window size for calculating a moving average temperature of the battery based on the time series temperature data acquired in operation 410.

[0082] At operation 430, the electronic device (100) can calculate a moving average temperature of the battery based on the window size calculated at operation 420.

[0083] In operation 440, the electronic device (100) can diagnose an abnormality in the battery based on the moving average temperature calculated in operation 430.

[0084] The electronic device according to the above-described embodiments may include a processor, a memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with an external device, a user interface device such as a touch panel, a key, an icon, etc. The methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable codes or program instructions executable on the processor. Here, the computer-readable recording medium includes a magnetic storage medium (e.g., read-only memory (ROM), random-access memory (RAM), floppy disk, hard disk, etc.) and an optical reading medium (e.g., CD-ROM, DVD: Digital Versatile Disc)). The computer-readable recording medium may be distributed to computer systems connected to a network, so that the computer-readable code may be stored and executed in a distributed manner. The medium may be readable by a computer, stored in a memory, and executed by a processor.

[0085] Various embodiments of the present disclosure may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various hardware and / or software components that perform specific functions. For example, embodiments may employ direct circuit components, such as memory, processing, logic, look-up tables, etc., that may perform various functions under the control of one or more microprocessors or other control devices. Similarly, the present embodiments may be implemented in a programming or scripting language, such as C, C++, Java, or an assembler, including various algorithms implemented as a combination of data structures, processes, routines, or other programming components. Functional aspects may be implemented as algorithms that execute on one or more processors. Furthermore, the present embodiments may employ conventional techniques for electronic configuration, signal processing, and / or data processing. Terms like "mechanism," "element," "means," and "composition" can be used broadly and are not limited to mechanical or physical components. These terms can also encompass a series of software routines, such as those associated with a processor.

[0086] The above-described embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

1. In electronic devices, Information acquisition interface; and comprising a processor operatively connected to the above information acquisition interface; The above processor, Using the above information acquisition interface, time series temperature data of the battery is acquired, Based on the above time series temperature data, a window size for calculating a moving average temperature of the battery is calculated, Based on the above-described window size, the moving average temperature of the battery is calculated, An electronic device that diagnoses an abnormality in the battery based on the calculated moving average temperature.

2. In paragraph 1, The above time series temperature data continuously represents the temperature of the battery over time, The above window size is an electronic device that represents the length of time in a time interval based on the time point to be diagnosed.

3. In paragraph 1, The above time series temperature data discretely represents the temperature of the battery over time according to the temperature measurement cycle, The above window size is an electronic device that represents the number of temperatures of the battery over time.

4. In paragraph 1, The above processor, Calculate the slope between the first temperature and the second temperature of the above time series temperature data, Calculate the window size based on the calculated slope, The above first temperature is the temperature of the battery at the first point in time, An electronic device wherein the second temperature is the temperature of the battery at a second point in time corresponding to a diagnostic target point in time after a specified time from the first point in time.

5. In paragraph 4, The above processor, An electronic device that calculates the window size so that the calculated slope and the window size are inversely proportional.

6. In paragraph 5, The above processor, An electronic device that calculates the window size based on the following mathematical expression 1. [Mathematical Formula 1] (In Equation 1, N p is the above window size, N s is the preset initial window size, S is the calculated slope.) 7. In paragraph 1, The above processor, An electronic device that diagnoses an abnormality in the battery by comparing the temperature of the battery at the time of diagnosis with the calculated moving average temperature.

8. In paragraph 7, The above processor, An electronic device that diagnoses the battery as an abnormal battery when the temperature of the battery at the diagnosis target point in time is higher than the calculated moving average temperature.

9. In a battery diagnosis method performed by an electronic device, An action to acquire time series temperature data of a battery; An operation of calculating a window size for calculating a moving average temperature of the battery based on the time series temperature data; An operation of calculating the moving average temperature of the battery based on the calculated window size; and A battery diagnosis method, comprising an operation of diagnosing an abnormality of the battery based on the calculated moving average temperature.

10. In paragraph 9, The above time series temperature data continuously represents the temperature of the battery over time, A battery diagnosis method, wherein the above window size represents the length of time in a time interval based on the diagnosis target time point.

11. In paragraph 9, The above time series temperature data discretely represents the temperature of the battery over time according to the temperature measurement cycle, A battery diagnostic method, wherein the above window size represents the number of temperatures of the battery over time.

12. In paragraph 9, The operation of calculating the above window size is: An operation for calculating a slope between a first temperature and a second temperature of the above time series temperature data, and An operation of calculating the window size based on the calculated slope is included, The above first temperature is the temperature of the battery at the first point in time, A battery diagnosis method, wherein the second temperature is the temperature of the battery at a second point in time corresponding to a diagnosis target point in time after a specified time from the first point in time.

13. In paragraph 12, The operation of calculating the above window size is: A battery diagnosis method, comprising an operation of calculating the window size so that the calculated slope and the window size are inversely proportional.

14. In paragraph 13, The operation of calculating the above window size is a battery diagnosis method based on the following mathematical expression 1. [Mathematical Formula 1] (In Equation 1, N p is the above window size, N s is the preset initial window size, S is the calculated slope.) 15. In paragraph 9, The operation to diagnose the above battery abnormality is as follows: A battery diagnosis method, comprising an operation of diagnosing the battery as an abnormal battery if the temperature of the battery at the diagnosis target point in time is higher than the calculated moving average temperature.

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