Patent Publication Number: US-10324139-B2

Title: Method and electronic device for detecting internal short circuit in battery

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS AND CLAIM OF PRIORITY 
     This application is related to and claims priority to from Indian Patent Application No. 201641044470, filed on Dec. 27, 2016 and Indian Patent Application No. 201641044470, filed on Dec. 22, 2017, the contents of which are incorporated herein by reference. 
     TECHNICAL FIELD 
     The present disclosure relates to battery management systems. More particularly, the present disclosure relates to a method and electronic device detecting internal short circuit in a battery. 
     BACKGROUND 
     Batteries are made of one or more cells, for example, lithium-ion cells or lithium-air cells, and are used to power many different types of devices. Rechargeable lithium batteries are attractive energy storage devices for portable electric and electronic devices. As the energy of batteries continues to rise, the potential for serious failures becomes a concern. Due to manufacturing variability, extreme operating conditions, exploitative usage and so on, a battery of the electronic may develop faults. 
     Catastrophic battery failure may include a thermal runaway event in which an internal short circuit inside a cell initiates a self-accelerating decomposition reaction inside the cell. The thermal runaway events may include smoke, flames, or even an explosion if an intervention is not performed in a timely manner. 
     In conventional systems, various mechanisms are available to detect short circuits in cells by monitoring cell voltages. 
     Voltage of a cell is monitored over time when there is no charge or discharge current flowing in the cell. A drop or decline in cell voltage beyond a certain value can indicate the presence of an internal short in the cell, thus allowing the cell to be rejected as faulty cell. However, such voltage tests do not identify cells that will develop internal short circuits later in their life cycle, which may lead to catastrophic failures of cells that develop internal short circuits during operation. Early detection of the fault is essential to ensure safe operation of the battery. Detection of fault inception due to battery internal short is difficult due to lack of prior information. 
     The above information is presented as background information only to help the reader to understand the present invention. Applicants have made no determination and make no assertion as to whether any of the above might be applicable as Prior Art with regard to the present application. 
     SUMMARY 
     The principal object of the embodiments herein is to provide a method and electronic device for detecting internal short circuit in a battery. 
     Another object of the embodiments herein is to estimate an internal resistance of the battery using battery gauge data such as voltage, current and temperature and so on. 
     Another object of the embodiments herein is to apply Haar transform on the estimated internal resistance to measure discontinuity in the internal resistance during a charge-discharge transition. 
     Another object of the embodiments herein is to identify a magnitude of change in the internal resistance based on the Haar transform. 
     Another object of the embodiments herein is to compare a change in the internal resistance of the battery with a pre-defined resistance change threshold value to detect the internal short circuit in the battery. 
     Another object of the embodiments herein is to indicate the detected short circuit in the battery to a user. 
     Another object of the embodiments herein is to provide an alert message to the user about failure of the battery. 
     Another object of the embodiments herein is to provide an indication to the user about scheduling replacement of the battery. 
     Another object of the embodiments herein is to indicate the user about safety limit of charging the battery according to a minimum voltage. 
     Another object of the embodiments herein is to determine a fault index value of a plurality of batteries. 
     Another object of the embodiments herein is to identify the plurality of batteries as faulty batteries by comparing the fault index value with a pre-defined fault index value. 
     Accordingly embodiments herein provide a method for detecting an internal short circuit in a battery. The method includes obtaining, by a battery management system, battery gauge data. Further, the method includes estimating, by the battery management system, an internal resistance of the battery using the battery gauge data. Furthermore, the method includes detecting, by the battery management system, the internal short circuit in the battery by comparing a change in the internal resistance with a pre-defined resistance change threshold value. 
     Accordingly the embodiments herein provide a method for detecting an internal short circuit in a battery. The method includes obtaining battery gauge data from each battery among a plurality of batteries. The method includes estimating an internal resistance of each battery using the battery gauge data. The method includes detecting the internal short circuit in the battery by comparing a change in the internal resistance with a pre-defined resistance change threshold value. Further, the method includes determining a fault index value of a plurality of batteries. Furthermore, the method includes identifying the plurality of batteries as faulty batteries by comparing the fault index value with a pre-defined fault index value. 
     Accordingly the embodiments herein provide an electronic device for detecting an internal short circuit in a battery. The electronic device includes a battery management system configured to obtain battery gauge data. Further, the battery management system configured to estimate an internal resistance of the battery using the battery gauge data. The internal resistance is estimated based on a filtering technique applied to an electrochemical model. Furthermore, the battery management system configured to detect the internal short circuit in the battery by comparing a change in the internal resistance with a pre-defined resistance change threshold value. 
     Accordingly the embodiments herein provide an electronic device for detecting an internal short circuit in a battery. The electronic device includes a battery management system configured to obtain battery gauge data from each battery among a plurality of batteries. The battery management system configured to estimate an internal resistance of each battery using the battery gauge data. The internal resistance is estimated based on a filtering technique applied to an electrochemical model. The battery management system configured to detect the internal short circuit in the battery by comparing a change in the internal resistance with a pre-defined resistance change threshold value. Further, the battery management system configured to determine a fault index value of a plurality of batteries. Furthermore, the battery management system configured to identify the plurality of batteries as faulty batteries by comparing the fault index value with a pre-defined fault index value. 
     These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       This method is illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which: 
         FIG. 1  is a block diagram illustrating various hardware elements of an electronic device, according to an embodiment as disclosed herein; 
         FIG. 2  is a block diagram illustrating various hardware elements of a battery management system, according to an embodiment as disclosed herein; 
         FIG. 3  is a flow chart illustrating a method for detecting an internal short circuit in a battery, according to an embodiment as disclosed herein; 
         FIG. 4  is a flow chart illustrating a method for determining a pre-determined threshold value for detecting the battery internal short circuit in the battery, according to an embodiment as disclosed herein; 
         FIGS. 5A-5E  are example illustrations in which the electronic device indicates the detected internal short circuit in the battery, according to an embodiment as disclosed herein; 
         FIG. 6  is a flow chart illustrating a method for detecting internal short circuit for a plurality of batteries, according to an embodiment as disclosed herein; 
         FIGS. 7A-7D  are graphs showing Haar transform of internal resistance, according to an embodiment as disclosed herein; 
         FIG. 8A  is a graph showing variation of voltage for a normal battery and a faulty battery, according to an embodiment as disclosed herein; 
         FIG. 8B  is a graph showing the internal resistance of the normal battery and the faulty battery, according to an embodiment as disclosed herein; and 
         FIG. 8C  is a graph showing a second Haar transform coefficient for the normal battery and the faulty battery, according to an embodiment as disclosed herein. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. In the following description, specific details such as detailed configuration and components are merely provided to assist the overall understanding of these embodiments of the present disclosure. Therefore, it should be apparent to those skilled in the art that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness. 
     Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. Herein, the term “or” as used herein, refers to a non-exclusive or, unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein. 
     As is tradition in the field, embodiments may be described and illustrated in terms of blocks which carry out a described function or functions. These blocks, which may be referred to herein as managers, units or modules or the like, are physically implemented by analog and/or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the disclosure. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure. 
     The embodiments herein provide a method for detecting an internal short circuit in a battery. The method includes obtaining, by a battery management system, battery gauge data. Further, the method includes estimating, by the battery management system, an internal resistance of the battery using the battery gauge data. The internal resistance is estimated based on a filtering technique applied to an electrochemical model. Furthermore, the method includes detecting, by the battery management system, the internal short circuit in the battery by comparing a change in the internal resistance with a pre-defined resistance change threshold value. 
     Unlike conventional systems and methods, the proposed system and method can be used for early detection of the internal short circuit in the battery. The proposed method does not require additional hardware or modification of existing hardware in the electronic device. The proposed method can be combined with any state of the art electrochemical battery model for estimating the internal resistance of the battery and to determine the internal short circuit in the battery. The proposed method can be used on board for internal short detection in real time. 
     Unlike conventional systems and methods, the proposed method can be used to measure change or variation or discontinuity in the internal resistance (during a charge-discharge transition) by applying the Haar transform on estimated internal resistance signal of the battery. The Haar transform localizes and magnifies the internal resistance signal of the battery. The coefficients of the Haar transform are determined, for example using a Fast Haar Transform. Further, the peaks of the Haar transform coefficients are used for the internal short circuit detection in the battery. 
     In an embodiment, the battery gauge data is stored periodically during charge-discharge transition for detecting the internal short circuit in the battery. 
     In another embodiment, the battery gauge data is periodically transmitted to a server for indicating the internal short circuit in the battery. 
     In an embodiment, the proposed method includes indicating the detected internal short circuit in the battery on the screen of the electronic device. 
     In an embodiment, the proposed method includes indicating the user to schedule replacement of the battery for optimal performance. 
     In some embodiments, the proposed method includes transmitting an indication about the detected short circuit to one or more connected devices of the user. 
     In various embodiments, the detection of internal short circuit is performed on a plurality of batteries for identifying the faulty batteries among the plurality of batteries. The method includes obtaining battery gauge data from each battery among a plurality of batteries. The method includes obtaining battery gauge data from each battery among a plurality of batteries. The method includes estimating an internal resistance of each battery using the battery gauge data. The internal resistance is estimated based on a filtering technique applied to an electrochemical model. The method includes detecting the internal short circuit in the battery by comparing a change in the internal resistance with a pre-defined resistance change threshold value. Further, the method includes determining a fault index value of a plurality of batteries. Furthermore, the method includes identifying the plurality of batteries as faulty batteries by comparing the fault index value with a pre-defined fault index value. 
     Unlike conventional systems and methods, the proposed method can be used for testing the batteries in a batch to identify faulty batteries in the batch. The proposed method of internal short detection is performed on the batteries to identify the faulty batteries from the plurality of batteries. 
     Further, the proposed method can be used to indicate the faulty batteries among the plurality of batteries and various statistical data related to number of cycles between the fault detection and the battery failure. 
     Referring now to the drawings, and more particularly to  FIGS. 1 through 8C , there are shown preferred embodiments. 
       FIG. 1  is a block diagram illustrating various hardware elements of an electronic device  100 , according to an embodiment as disclosed herein. 
     In an embodiment, the electronic device  100  can be, for example, a mobile phone, a smart phone, a laptop, a desktop computer Personal Digital Assistants (PDAs), a tablet, a phablet, a consumer electronic device, a dual display device, edge display, or any other electronic device. In another embodiment, the electronic device  100  can be a wearable device such as, for example, a smart watch, a smart bracelet, a smart glass, or the like. In yet another embodiment, the electronic device  100  can be Internet of things (IoT) device. 
     The electronic device  100  includes a battery  110 , an interface unit  120 , and a battery management system  130 . Further, the electronic device  100  includes (or, be associated with), a display unit  140  (e.g., a Cathode Ray Tube (CRT) display, a Liquid Crystal Display (LCD), Organic Light-Emitting Diode (OLED), a Light-emitting diode (LED), etc.), being interfaced with a processor  150  (e.g., Central processing unit (CPU), Graphics processing unit (GPU), hardware chipset, etc.) communicatively coupled to a memory  160  (e.g., a volatile memory and/or a non-volatile memory). The memory  160  includes storage locations configured to be addressable through the processor  150 . 
     In an embodiment, the battery  110  can be for e.g., a rechargeable battery with a predefined capacity set by the battery manufacturer or Original Equipment Manufacturer (OEM). The characteristics of the battery  110  (provided by the battery manufacturer/OEM) includes, for e.g., type of battery (e.g., Lithium), total capacity (mAh) of the battery  110 , constant current rate, nominal voltage level, charge allowance capacity, preset charge capacity, rated capacity of the battery, charge limit voltage, discharge limit voltage, maximum continuous charge current, max continuous discharge current, initial impedance, charging/discharging life cycles, or the like. 
     The interface unit  120  can be configured to communicate with the battery  110  to continuously receive the battery gauge data (i.e., a plurality of battery parameters). Further, the battery management system  130  can be configured to communicate with the interface unit  120  to extract the plurality of battery parameters. 
     In an embodiment, the battery management system  130  can be integrated with the battery  110  (e.g., battery management system chipset). The battery management system  130  can be configured to determine the internal resistance of the battery  110  using the battery gauge data. It should be noted that the internal resistance is estimated using any conventional electrochemical battery model and a filtering technique is applied to the estimated internal resistance to remove the noise in the internal resistance signal. 
     Further, the battery management system  130  can be configured to apply the Haar transform on the internal resistance of the battery  110 . The Haar transform localizes and magnifies the internal resistance signal. The coefficients of the Haar transform are determined using a Fast Haar Transform. The battery management system  130  utilizes the peaks of the Haar transform coefficients for the internal short circuit detection in the battery  110 . The battery management system  130  can be configured to perform one or more actions to detect the internal short circuit in the battery as detailed in conjunction with the  FIG. 2 . 
     The display unit  140  can be configured to indicate the detected internal short circuit on the screen of the electronic device. Further, the display unit  140  can be configured to indicate one or more battery parameters such as battery usage interval, battery charge indication, present state of the battery, battery indicator (i.e., graphical, percentage, etc.), remaining battery, battery status, battery drain notifications, etc. 
     The processor  150  can be configured to communicate with all the hardware elements in the electronic device  100  to perform the functionalities of the corresponding elements. 
     In an embodiment, the battery gauge data is stored periodically during charge-discharge transition in the memory  160  for detecting the internal short circuit in the battery. The memory  160  may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory  160  may, in some examples, be considered a non-transitory storage medium. However, the term “non-transitory” should not be interpreted that the memory  160  is non-movable. In some examples, the memory  160  can be configured to store larger amounts of information than the memory. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). 
       FIG. 2  is a block diagram illustrating various hardware elements of a battery management system  130 , according to an embodiment as disclosed herein. 
     Referring to  FIG. 2 , the battery management system  130  includes a battery parameter measurement unit  131 , an internal short detection unit  132  and a short detection indication unit  133 . 
     The battery parameter measurement unit  131  can be configured to measure the battery gauge data (i.e., the plurality of battery parameters). In an embodiment, the plurality of battery parameters include for e.g., voltage, current, temperature, total battery capacity, an internal impedance, a state of charge of the battery, a number of charging cycles, a number of discharging cycles, a discharge capacity, usage information of the electronic device  100 , and usage information of each application installed in the electronic device  100 , or the like. 
     Further, the battery parameter measurement unit  131  can be configured to measure the present temperature of the electronic device  100 /battery  110 . In an embodiment, the temperature is measured/determined by; monitoring temperature of the electronic device  100 /monitoring temperature of the battery  110 , an ambient temperature of the electronic device  100 /battery  110 , a current consumption and a charging temperature of the electronic device  100 /battery  110 , or the like. 
     The battery parameter measurement unit  131  can be configured to transmit battery gauge data (i.e., the measured battery parameters, corresponding temperature, voltage and total charge) to the internal short detection unit  132 . 
     The internal short detection unit  132  includes an internal resistance estimation unit  132 ( a ), a signal processing unit  132 ( b ), and an internal resistance comparison unit  132 ( c ). 
     In an embodiment, the internal resistance estimation unit  132 ( a ) can be configured to estimate the internal resistance of the battery using the battery gauge data. The internal resistance is estimated based on the filtering technique applied to an electrochemical model. The internal resistance is estimated using Scalar Kalman Filter method applied to the electrochemical model, using the battery gauge data. 
     In an embodiment, three parameters reduced order model (S3ROM) is used as the battery model. 
     
       
         
           
             
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     In an embodiment, least square error minimization is used for parameter estimation. The least square minimization of model prediction facilitates robust and consistent prediction. 
     Further, a scalar Kalman Filter is used for estimation of the internal resistance of the battery  110 . 
     The signal processing unit  132 ( b ) can be configured to apply Haar transform on the estimated internal resistance of the battery  110  to measure discontinuity in the internal resistance during a charge-discharge transition. The charge-discharge transition denotes the state in which the electronic device  100  is unplugged from charging, where as the discharge to charge transition indicates the state in which the electronic device  100  is plugged for charging. The signal processing unit  132 ( b ) can be configured to apply Haar transform on the estimated internal resistance of the battery at the charge-discharge transition. 
     Unlike to conventional methods and conventional systems, the proposed method can be used to measure discontinuity in the internal resistance during a charge-discharge transition by applying the Haar transform on the estimated internal resistance of the battery  110 . 
     Further, the signal processing unit  132 ( b ) can be configured to identify the magnitude of the change in the internal resistance based on the applied Haar transform. When Haar transform is applied on the internal resistance, the magnitude of the change in the internal resistance of the battery can be determined. In an embodiment, the signal processing unit  132 ( b ) can be configured to determine a second Haar coefficient of the internal resistance signal for identifying the magnitude of the change in the internal resistance of the battery  110  for detecting the internal short circuit in the battery  110 . 
     In an embodiment, the signal processing unit  132 ( b ) can be configured to determine the likelihood ratio, using the second Haar coefficient (h2) denoted as 
     
       
         
           
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     The internal resistance comparison unit  132 ( c ) can be configured to compare the magnitude of change in internal resistance with the pre-determined resistance change threshold value. For example, the pre-determined resistance change threshold value can be 0.5. The internal resistance comparison unit  132 ( c ) can be configured to determine whether the magnitude of change in internal resistance is less than 0.5. In case, when the internal resistance comparison unit  132 ( c ) determines that the magnitude of change in internal resistance is less than 0.5, then the internal resistance comparison unit  132 ( c ) detects the internal short circuit in the battery  110 . It should be noted that the pre-determined resistance change threshold value can be for example, 0.5 for a healthy battery and for a faulty battery, the magnitude of change in internal resistance is less than 0.5. Thus, the internal resistance comparison unit  132 ( c ) can be configured to compare the magnitude of change in internal resistance with the pre-determined resistance change threshold value to detect the internal short circuit in the battery. 
     In an embodiment, the short detection indication unit  133  can be configured to indicate the detected short circuit to the user. For example, the short detection indication unit  133  can be configured to generate an alert message to the user indicating the detected short circuit in the battery. In some embodiments, the short detection indication unit  133  can be configured to perform one or more actions to indicate the detected short circuit to the user, as explained in conjunction with  FIGS. 5A-5E . 
       FIG. 3  is a flow chart  300  illustrating a method for detecting an internal short circuit in a battery, according to an embodiment as disclosed herein. 
     Referring to the  FIG. 3 , at step  302 , the method includes obtaining the battery gauge data (i.e., the plurality of battery measurement parameters). In an embodiment, the method allows the battery management system  130  to obtain the battery gauge data. 
     At step  304 , the method includes estimating the internal resistance of the battery using the battery gauge data based on the filtering technique applied to the electrochemical model. In an embodiment, the method allows the battery management system  130  to estimate the internal resistance of the battery  110  using the battery gauge data based on the filtering technique applied to the electrochemical model. 
     At step  306 , the method includes applying the Haar transform on the estimated internal resistance to measure discontinuity in the internal resistance during a charge-discharge transition. In an embodiment, the method allows the battery management system  130  to apply the Haar transform on the estimated internal resistance to measure change or variation or discontinuity in the internal resistance during a charge-discharge transition. 
     Unlike conventional systems and methods, the proposed method can be used to measure discontinuity in the internal resistance during a charge-discharge transition by applying the Haar transform on estimated internal resistance signal of the battery. The Haar transform localizes and magnifies the internal resistance signal of the battery. The coefficients of the Haar transform are determined, for example using a Fast Haar Transform. Further, the peaks of the Haar transform coefficients are used for the internal short circuit detection in the battery. 
     At step  308 , the method includes identifying the magnitude of the change in the internal resistance based on the Haar transform. In an embodiment, the method allows the battery management system  130  to identify the magnitude of the change in the internal resistance based on the Haar transform. 
     At step  310 , the method includes determining whether the magnitude of the change in the internal resistance is less than the pre-determined resistance change threshold value. In an embodiment, the method allows the battery management system  130  to determine whether the magnitude of the change in the internal resistance is less than the pre-determined resistance change threshold value. 
     In case, it is determined that the magnitude of the change in the internal resistance is less than the pre-determined resistance change threshold value, then at step  312 , the method includes indicating the detected short circuit in the battery  110  to the user. In an embodiment, the method allows the battery management system  130  to indicate the detected short circuit in the battery  110  to the user. 
     In case, it is determined that the magnitude of the change in the internal resistance is greater than the pre-determined resistance change threshold value, then the method loops back to step  302 . 
     Unlike conventional systems and methods, the proposed system and method can be used for early detection of the internal short circuit in the battery. 
     The various actions, acts, blocks, steps, or the like in the flow diagram  300  may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention. 
       FIG. 4  is a flow chart  400  illustrating a method for determining a pre-determined resistance change threshold value for detecting the battery internal short circuit in the battery, according to an embodiment as disclosed herein. 
     Referring to the  FIG. 4 , in order to obtain the pre-determined resistance change threshold value, initially, at step  402 , the method includes obtaining normal battery gauge data and faulty battery gauge data which includes battery parameters such as for e.g., voltage, current, temperature, total battery capacity, an internal impedance, a state of charge of the battery, a number of charging cycles, a number of discharging cycles, a discharge capacity, usage information of the electronic device  100 , and usage information of each application installed in the electronic device  100 , or the like. It should be noted that these battery parameters are obtained for normal battery and the faulty battery. In an embodiment, the method allows the battery management system  130  to obtain normal battery gauge data and the faulty battery gauge data. 
     At step  404 , the method includes determining the second Haar coefficient of internal resistance for the normal battery gauge data and the faulty battery gauge data. In an embodiment, the method allows the battery management system  130  to determine second Haar coefficient of internal resistance for the normal battery gauge data and the faulty battery gauge data. 
     At step  406 , the method includes determining mean and standard deviation of the second Haar coefficient at charge-discharge transition for the normal battery and the faulty battery. In an embodiment, the method allows the battery management system  130  to determine the mean and standard deviation of the second Haar coefficient at charge-discharge transition for the normal battery and the faulty battery. The ratio of the second Haar coefficient for the normal battery and the second Haar coefficient for the faulty battery denotes the pre-defined resistance change threshold value. 
     The various actions, acts, blocks, steps, or the like in the flow diagram  400  may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention. 
       FIGS. 5A-5E  are example illustrations in which the electronic device  100  indicates the detected internal short circuit in the battery, according to an embodiment as disclosed herein. 
     Referring to the  FIG. 5A , the electronic device  100  indicates the detected internal short circuit in the battery on the screen of the electronic device  100 . The user can view the details of the detected short circuit by interacting with the view button. 
     In another embodiment, after detecting the internal short circuit, the electronic device  100  indicates the user to replace the battery for optimal performance as shown in the  FIG. 5B . Further, the user can view the details about the performance of the battery by interacting with the view button. 
     In another embodiment, the electronic device  100  indicates the detected internal short circuit in the battery  110  and suggests the user to charge the battery  110  up to 70% to ensure longer battery life as shown in the  FIG. 5C . Thus, the electronic device  100  indicates safety limit of charging the battery  110  to the user as shown in the  FIG. 5C  by suggesting the user to charge the battery  110  to a certain level according to a voltage level, for example 4.1 V. 
     In another embodiment, as shown in the  FIG. 5D , the electronic device  100  indicates the detected internal short circuit to the one or more connected devices of the user such as for e.g., a wearable device, a smart television or the like. For example, the electronic device  100  indicates the detected internal short circuit to the one or more connected devices through short range communication (SRC) technologies such as Bluetooth®, Wireless-Fidelity (Wi-Fi®), Near field communication (NFC) or the like. 
     In another embodiment, the electronic device  100  indicates the battery gauge data periodically to a server as shown in the  FIG. 5E . In some embodiments, the electronic device  100  can be configured to receive one or more updates to a battery application installed on the electronic device to ensure safe operation of the battery  110 . 
       FIG. 6  is a flow chart  600  illustrating a method for detecting internal short circuit for a plurality of batteries, according to an embodiment as disclosed herein. The flow chart  600  describes the various steps for testing the batteries in a batch to identify faulty batteries in the batch. The proposed method of internal short detection is performed on the batteries to identify the faulty batteries from the plurality of batteries. 
     Referring to the  FIG. 6 , at step  602 , the method includes obtaining battery gauge data from each battery among a plurality of batteries. In an embodiment, the method allows the battery management system  130  to obtain the battery gauge data from each battery among a plurality of batteries. 
     At step  604 , the method includes estimating the internal resistance of each battery using the battery gauge data based on a filtering technique applied to an electrochemical model. In an embodiment, the method allows the battery management system  130  to estimate the internal resistance of each battery using the battery gauge data based on a filtering technique applied to an electrochemical model. 
     At step  606 , the method includes detecting the internal short in the battery when the magnitude of the change in internal resistance is less than a pre-determined threshold value. In an embodiment, the method allows the battery management system  130  to detect the internal short in the battery when the magnitude of the change in internal resistance is less than a pre-determined threshold value. In case at step  606 , if the short is detected in the battery, at step  608 , the method includes determining fault index value of the plurality of batteries. In an embodiment, the method allows the battery management system  130  to determine fault index value of the plurality of devices. 
     At step  610 , the method includes identifying the plurality of batteries as faulty batteries by comparing the fault index value with the pre-defined fault index value. In an embodiment, the method allows the battery management system  130  to identify the plurality of batteries as faulty batteries by comparing the fault index value with a pre-defined fault index value. The battery management system  130  can be configured to determine whether the PPM of each battery is less than the pre-defined PPM value. In case, the battery management system  130  determines that the PPM of each battery is less than the pre-defined PPM value, then at step  612 , the method includes indicating the identified faulty batteries to the user. In an embodiment, the method allows the battery management system  130  to indicate the identified faulty batteries to the user. For example, the electronic device  100  indicates the faulty batteries and various statistical data related to number of cycles between the fault detection and the battery failure. The user can reject the batch of batteries in case the faulty batteries are identified. 
     The various actions, acts, blocks, steps, or the like in the flow diagram  600  may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention. 
       FIGS. 7A-7D  are graphs showing Haar transform of internal resistance, according to an embodiment as disclosed herein. 
     The internal resistance signal of the battery  110  is spectrally expanded as
 
 x ( t )=Σ i=1   n   &lt;x,H   i   &gt;H   i  
 
     The battery management system  130  can be configured to apply the Haar transform on the internal resistance of the battery  110 . The Haar transform localizes and magnifies the internal resistance signal. The coefficients of the Haar transform are obtained using a Fast Haar Transform. Peaks of the Haar transform coefficients are used for the internal short circuit detection in the battery  110 . The various Haar transform coefficients with respect to the internal resistance of the battery are shown in the  FIGS. 7A-7D . The peaks of the second Haar coefficient (H 2 ) of the internal resistance signal as shown in the  FIG. 7B  is analyzed to determine the internal short circuit in the battery. 
       FIG. 8A  is a graph showing variation of voltage for a normal battery and a faulty battery, according to an embodiment as disclosed herein. 
     Referring to the  FIG. 8A , for a given state of charge (SOC), over potential is represented as Vexp−Vocv. From the  FIG. 8A , it should be noted that the magnitude of over-potential is higher during discharge for the faulty batteries compared to the normal batteries. 
       FIG. 8B  is a graph showing the internal resistance of the normal battery and the faulty battery, according to an embodiment as disclosed herein. 
     Referring to the  FIG. 8B , for faulty batteries, the slope of the change in the internal resistance change during discharge is higher than the normal batteries. 
       FIG. 8C  is a graph showing a second Haar transform coefficient for the normal battery and the faulty battery, according to an embodiment as disclosed herein. From the  FIG. 8C , it can be inferred that there are two peaks in the normal cells and one peak for the faulty cells. Further, it should be noted that the change in the internal resistance during charge-discharge transition is low for the faulty cells. 
     The embodiments disclosed herein can be implemented using at least one software program running on at least one hardware device and performing network management functions to control the elements. 
     The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.