DEVICE AND METHOD FOR DETECTING AN ABNORMAL CONDITION OF A BATTERY USING A VOLTAGE DEVIATION VARIATION

Abstract

The present disclosure relates to a device and a method for detecting a battery anomaly using a voltage variation. According to one embodiment of the present disclosure, the device includes: a memory adapted to store at least one instruction for detecting a battery anomaly using a voltage variation; and a processor adapted to perform an operation according to the instruction, wherein the processor is adapted to: calculate a voltage change of the battery and store the calculated voltage change as a variable; and calculate a differential voltage variation (DDVD), which is a change in the voltage difference between respective cells within a battery module, and detect a battery anomaly using the voltage variation (DDVD) and the stored variable.

Description

[BACKGROUND OF THE INVENTION] 1. Field of the invention

[0001] The present disclosure relates to a device and a method for detecting a battery anomaly using a voltage deviation variation. 2. Description of the state of the art

[0002] To ensure the safety and performance of batteries used in various industries and electronic devices, battery anomaly detection technologies are essential. A battery is a device that stores and releases energy through internal chemical reactions. If excessive heat is generated during these energy storage and release processes, risks such as fire or explosion can arise. Therefore, battery anomaly detection technologies can detect such risks in advance and prevent accidents.

[0003] Lithium-ion batteries are efficient due to their high energy density, but serious safety issues can arise if they are overcharged, over-discharged, or short-circuited. Battery anomaly detection technologies can identify these problems at an early stage, protecting users and the environment. Additionally, these technologies can analyze the battery's condition, detect early signs of failure, and enable preventative maintenance when necessary. This can prevent abnormal battery operation and extend its lifespan.

[0004] Meanwhile, conventional battery anomaly detection methods primarily detect voltage changes by setting a fixed threshold. In such conventional methods, if a voltage change exceeds the fixed threshold, the battery is determined to be anomalous. However, because it is difficult to set the threshold appropriately for all operating conditions, accurately diagnosing the battery's health and detecting anomalies is challenging. In particular, voltage changes can vary significantly depending on factors such as the battery's state of charge, the operating environment, and the temperature. Since the fixed threshold in conventional detection methods does not adequately reflect these various factors, accurate diagnosis and anomaly detection are difficult.

[0005] Furthermore, when a battery experiences large current changes, even a normal cell can exceed a fixed threshold. For example, during rapid charging or high-power discharge, even a healthy battery can exhibit a large voltage change. In such cases, however, relying solely on a fixed threshold increases the likelihood of misdiagnosing a normal battery as faulty.

[0006] Furthermore, conventional battery anomaly detection methods cannot distinguish between voltage changes that occur naturally due to battery aging and those caused by actual anomalies, making accurate battery health diagnosis difficult. An aged battery may exhibit a different voltage change pattern, but it is difficult to differentiate voltage and current changes caused by aging using only a fixed threshold. [SUMMARY OF THE INVENTION]

[0007] According to one aspect of the present disclosure, a device and a method for detecting signs of a battery anomaly before a fire occurs are provided by setting a variable threshold based on a current change using a relational equation of a battery equivalent model and detecting the battery anomaly based on the set variable threshold.

[0008] According to another aspect of the present disclosure, errors in the calculated threshold can occur in cases where parameters used in the relational equation of the battery equivalent model vary depending on battery degradation or ambient temperature. In this respect, in some embodiments, monitoring changes in cell deviation within a battery module, instead of relying on the relational equation of a single cell, can reduce the effect of parameter errors using relative values.

[0009] A device for detecting a battery anomaly using a voltage variation according to an embodiment of the present disclosure may include: a memory adapted to store at least one instruction for detecting a battery anomaly using a voltage variation; and a processor adapted to perform an operation according to the instruction, wherein the processor may be adapted to: calculate a voltage change of the battery and store the calculated voltage change as a variable; and calculate a differential variation voltage detection (DDVD), which is a change in the voltage difference between respective cells within a battery module, and detect a battery anomaly using the differential variation voltage detection (DDVD) and the stored variables.

[0010] According to one embodiment, the processor may include a battery equivalent model voltage change calculation unit adapted to calculate a voltage change of the battery using parameters relating to a state of charge (SOC) of the battery when a current is applied to an equivalent circuit model (ECM).

[0011] According to one embodiment, the processor may include a distance reflection unit adapted to adjust, when an accumulated distance exceeds a predetermined value, a ratio of at least one of the calculated values ​​of the voltage change in the battery equivalent model by taking into account deterioration differences between cells within the battery module and setting the adapted calculated value as a voltage deviation variation (DDVD) criterion.

[0012] According to one embodiment, the parameters relating to the state of charge (SOC) can be a voltage change (Δt / C1 (I k - I k-1 )) due to the capacity of the battery equivalent model and circuit characteristics (1 - Δt / R1C1) of an RC circuit.

[0013] According to one embodiment, the driving distance reflection unit can be adapted to compare the calculated voltage deviation variation criterion with driving results of a normal battery and to adjust an increase or decrease of the criterion according to a current level.

[0014] A method for detecting a battery anomaly using a voltage variation according to an embodiment of the present disclosure may include: calculating a voltage change of the battery and storing the calculated voltage change as a variable; and calculating a differential variation voltage detection (DDVD), which is a change in the voltage difference between respective cells within a battery module, and detecting a battery anomaly using the differential variation voltage detection (DDVD) and the stored variables.

[0015] According to one embodiment, the step of detecting a battery anomaly may include: calculating, by a battery equivalent model voltage change calculation unit, a voltage change of the battery using parameters relating to a state of charge (SOC) of the battery when a current is applied to an equivalent circuit model (ECM).

[0016] According to one embodiment, the step of calculating a voltage change of the battery may include the following: when an accumulated driving distance exceeds a predetermined value, adjusting, by a driving distance reflection unit, a ratio of at least one of the calculated values ​​of the voltage change in the battery equivalent model by taking into account deterioration differences between cells within the battery module and setting the adjusted calculated value as a voltage deviation variation (DDVD) criterion.

[0017] According to one embodiment, the parameters relating to the state of charge (SOC) can be a voltage change (Δt / C1 (I k - I k-1 )) due to the capacity of the battery equivalent model and circuit characteristics (1 - Δt / R1C1) of an RC circuit.

[0018] According to one embodiment, the step of setting the adjusted calculated value as a voltage deviation variation (DDVD) criterion may include: comparing the calculated voltage deviation variation criterion with driving results of a normal battery; and adjusting an increase or decrease of the criterion according to a current level.

[0019] According to one embodiment, various embodiments of the present disclosure provide an effect of enabling adaptation to different operating conditions and changes in battery state by dynamically adjusting a threshold for determining a battery anomaly based on a current change.

[0020] According to one embodiment, various embodiments of the present disclosure can reduce battery anomaly detection errors by setting a voltage change, which serves as a criterion for anomaly determination, as a variable using a battery equivalent model instead of as a constant, thereby reflecting current usage patterns.

[0021] According to one embodiment, various embodiments of the present disclosure can manage the battery based on a voltage deviation variation instead of a voltage change, thereby further reducing anomaly detection errors caused by errors in parameters used in the battery equivalent model. [BRIEF DESCRIPTION OF THE DRAWINGS]

[0022] The above and other tasks, features and advantages of the present disclosure will become clearer from the following detailed description in conjunction with the accompanying drawings, in which: Fig. 1 a diagram illustrating a device for detecting a battery anomaly using a voltage variation (“battery anomaly detection device”) according to an embodiment of the present disclosure; Fig. 2 is a block diagram illustrating the battery anomaly detection device using a voltage variation according to an embodiment of the present disclosure; Fig. 3 is a diagram illustrating the configuration of a processor according to an embodiment of the present disclosure; Fig. 4 is a diagram showing test results that analyze the voltage deviation of a battery using the differential deviation voltage detection (DDVD) criterion according to an embodiment of the present disclosure; Fig. 5 is a diagram illustrating the voltage deviation variation criterion and the deviation change of a supervised equivalent circuit model (ECM) according to an embodiment of the present disclosure; and Fig. 6 is a flowchart illustrating a procedure for detecting a battery anomaly using a voltage variation variation (“battery anomaly”). [DETAILED DESCRIPTION OF THE INVENTION]

[0023] Exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. However, these embodiments are merely illustrative and the present disclosure is not limited to the specific embodiments described as examples.

[0024] Although terms like "first," "second," and the like may be used to describe different elements, components, and / or sections, these elements, components, and / or sections are not limited by these terms. These terms are used merely to distinguish one element, component, and / or section from another. Therefore, it is understood that the first element, component, or section mentioned below may also be a second element, component, or section within the technical concept of the present invention.

[0025] The terminology used herein serves only to describe certain embodiments and is not intended to limit the present invention. As used herein, singular forms are to include plural forms unless the context clearly indicates otherwise. It is further understood that the terms "comprises" and / or "made of," as used herein, do not preclude the presence or addition of one or more other components, steps, operations, and / or elements in addition to the explicitly mentioned component, step, operation, and / or element.

[0026] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as generally understood by those skilled in the art in the field to which the present invention relates. Terms defined in commonly used dictionaries should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0027] Fig. Figure 1 is a diagram illustrating a battery anomaly detection device using voltage variation according to an embodiment of the present disclosure.

[0028] With reference to Fig. 1. According to one embodiment of the present disclosure, the battery anomaly detection device detects signs of a battery anomaly based on differential voltage deviation detection (DDVD, hereinafter also referred to as voltage deviation variation (DDVD) or simply DDVD). DDVD is a criterion that monitors the condition of battery cells and detects anomalies in a battery management system (BMS). The DDVD criterion analyzes the voltage changes of battery cells to enable early detection of anomalous conditions or signs of failure. For example, the voltage of a battery cell changes during the charging and discharging processes and should remain within a normal range.The battery anomaly detection device according to one embodiment enables the detection of voltage changes that deviate from the normal range or change anomalously using the DDVD (voltage deviation variation), thereby determining whether the battery is anomalous. Additionally, the device of the present disclosure monitors voltage changes in real time using the DDVD, which is a voltage deviation variation, thus enabling the detection of anomalies even under rapidly changing conditions. Furthermore, the battery anomaly detection device according to one embodiment of the present disclosure can determine whether an anomaly is present based on the voltage deviation between a specific cell and surrounding cells.If the voltage of a specific cell shows a significant difference compared to other cells, the device of the present disclosure can determine that the cell shows signs of cell degradation or cell failure. Furthermore, if the voltage change exceeds a predetermined threshold, the battery anomaly detection device according to one embodiment of the present disclosure can determine that the corresponding cell is anomalous and issue a warning based on the DDVD criterion.

[0029] As in Fig. As shown in Figure 1, the battery anomaly detection device according to an embodiment of the present disclosure calculates a voltage deviation variation (DDVD) criterion using a current, state-of-charge (SOC)-related data (Pack-SOC), and a distance traveled, which is recorded by an odometer. In the present disclosure, the current refers to the current flowing through a battery module, which is a key parameter that significantly affects the voltage and the internal state of the battery. The SOC-related data (Pack-SOC) represent parameters that indicate the state of the battery, including at least one state-of-charge (SOC) value, voltage change due to capacity, and RC circuit characteristics. The distance traveled is collected by an odometer, which is a device adapted to measure the distance traveled by the battery module.In the case of an electric vehicle, the odometer is used to monitor battery degradation and performance changes according to the distance traveled.

[0030] In the present disclosure, the battery anomaly detection device calculates an ECM voltage deviation variation using current and state-of-charge (SOC)-related parameters. The ECM voltage deviation variation is calculated based on the current and SOC input data using an equivalent circuit model (ECM) to determine the battery's voltage change and deviation. The ECM represents the battery's electrical behavior through circuit components such as resistors and capacitors, thus indicating changes in the battery's state of charge. The battery anomaly detection device then applies a ratio factor according to the mileage driven; for example, it reflects the influence of mileage on the ECM voltage deviation variation by using a ratio corresponding to the mileage obtained from an odometer.This enables more accurate anomaly detection by taking into account battery degradation and performance changes that can occur as the driving distance increases. The battery anomaly detection device then calculates a voltage deviation variation (DDVD) criterion, which can be determined according to Equation 1. Voltage deviation variation (DDVD) criterion = ECM voltage deviation variation × odometer factor

[0031] According to one embodiment, the battery anomaly detection device calculates a voltage deviation variation (DDVD) criterion by multiplying an ECM voltage deviation variation by an odometer factor. The calculated voltage deviation variation criterion is used to dynamically detect voltage variations between battery cells, thereby enabling early detection of cell imbalance or anomalous conditions.

[0032] The battery anomaly detection device using voltage deviation variation according to an embodiment of the present disclosure uses various input data during the process of setting a voltage deviation variation (DDVD) criterion to accurately assess the battery's condition and enable early detection of anomalies. This approach can optimize battery safety, efficiency, and life cycle, and significantly improve the reliability of battery modules in electric vehicles and large-scale energy storage systems.

[0033] Fig. Figure 2 is a block diagram illustrating the battery anomaly detection device using a voltage variation according to an embodiment of the present disclosure.

[0034] As in Fig. As shown in Figure 2, the battery anomaly detection device of the present disclosure can include a communication module 110, a memory 120, and a processor 130. The configuration of the battery anomaly detection device using a voltage deviation variation, as shown in Figure 2, is described in Figure 2. Fig. Figure 2 is merely a simplified example. The communication module 110 can be configured in any communication mode, such as wired or wireless, and can be implemented through various communication networks, such as a Personal Area Network (PAN) or a Wide Area Network (WAN). Additionally, the communication module 110 can operate based on the World Wide Web (WWW) and can also use wireless transmission technologies for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth. For example, the communication module 110 can perform the transmission and reception of data required to execute a technique according to an embodiment of the present disclosure.

[0035] Memory 120 can refer to any type of storage medium. For example, Memory 120 can include at least one type of storage medium selected from flash memory, hard disk, multimedia card micro, card-type memory (e.g., SD or XD memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, or an optical disk. Memory 120 can also form or represent a database adapted to store various data required for the operation of the battery anomaly detection device 100.

[0036] Memory 120 can store at least one instruction executable by processor 130. Additionally, memory 120 can store any type of information generated or determined by processor 130 and any type of information received from a server (not shown). For example, memory 120 can store user-specific RM data and RM logs, as described below. Furthermore, memory 120 can store various types of modules, instruction sets, and models.

[0037] The processor 130 can perform the technical features of the present disclosure according to the embodiments described below by executing at least one instruction stored in the memory 120. In one embodiment, the processor 130 can be adapted to include at least one core and can include a data analysis and / or processing processor, such as a central processing unit (CPU), a general-purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU).

[0038] The Processor 130 can train a neural network or a model designed using machine learning or deep learning techniques. To this end, the Processor 130 can perform computations to train a neural network, including processing input data, extracting features from the input data, calculating errors, and updating the neural network's weights through backpropagation. Additionally, the Processor 130 can perform inference for a predetermined purpose using a model implemented based on techniques such as an artificial neural network.

[0039] In one embodiment, the processor 130 can calculate a voltage change in a battery and store the calculated voltage change as a variable. For this purpose, the voltage can be measured using a sensor or an analog-to-digital converter (ADC), and the processor 130 can read the measured voltage periodically. The voltage change can be calculated as the difference between a currently measured voltage and a previously measured voltage. For this purpose, the embodiment can store the previous voltage value and update the stored value when a new measurement is obtained. In one embodiment, the variable for storing the measured voltage change can be stored in memory, a file, a database, or the like.

[0040] Furthermore, the processor 130 can calculate a voltage deviation variation (DDVD), which represents a change in the voltage difference between cells within a battery module, and can detect a battery anomaly using the calculated voltage deviation variation (DDVD) and the stored variables.

[0041] The following describes a method for measuring voltage variation (DDVD) between individual cells within a battery module and for detecting battery anomalies based on this measurement. A battery pack contains multiple cells connected in series or parallel, and each cell may have a different voltage due to its chemical properties or external factors. This voltage difference can change over time, and monitoring such changes is important to ensure battery safety and performance. In one embodiment, the processor 130 can periodically measure the voltage of each battery cell. The measurement can be performed using a sensor, such as an analog-to-digital converter (ADC). The processor 130 can then collect and store voltage data for all cells in real time and calculate voltage differences between adjacent cells.For example, the 130 processor can calculate the voltage difference between cell 1 and cell 2, between cell 2 and cell 3, and so on. Additionally, the voltage deviation variation (DDVD) can be calculated by comparing the previously measured voltage difference with the currently measured voltage difference.

[0042] The range of voltage variation expected under normal conditions can be set. This setting can be based on experimental data or information provided by the manufacturer. Additionally, the threshold can be adjusted to take into account various factors such as temperature, load conditions, and the battery's lifespan. Furthermore, in one embodiment, if the voltage variation exceeds the set threshold, the device can determine that the cell is in an anomalous state. This could indicate a potential problem such as cell imbalance, cell damage, or overcharging and / or over-discharging. In another embodiment, when an anomalous state is detected, the system can issue a warning to the user and, if necessary, suspend battery charging and discharging or switch to a safe mode.

[0043] Fig. Figure 3 is a diagram illustrating the configuration of processor 130 according to an embodiment of the present disclosure.

[0044] With reference to Fig. 3. According to one embodiment, the processor 130 can be adapted to include a battery equivalent model voltage change calculation unit 131 and a distance reflection unit 133. The term "unit" as used here should be interpreted to include software, hardware, or a combination thereof, depending on the context in which the term is used. For example, software may include machine language, firmware, embedded code, or application software. As another example, hardware may include a circuit, a processor, a computer, an integrated circuit, an integrated circuit core, a sensor, a microelectromechanical system (MEMS), a passive device, or a combination thereof.

[0045] When a current is applied to the battery equivalent circuit model (ECM), the battery equivalent model voltage change calculation unit 131 can calculate a voltage change of the battery using parameters related to the state of charge (SOC). In one embodiment, the battery equivalent model voltage change calculation unit 131 can calculate the voltage change according to Equation 2. Vt,k−Vt,k−1=Rs(Ik−Ik−1)+Δt / C1(Ik−Ik−1)+(1−Δt / R1C1)(VL,k−1−VL,k−2)

[0046] The voltage change (V t,k - V t,k-1 ) in equation 2 represents the difference between the voltage at time k and the voltage at time k-1 and can be used to calculate the voltage change of a battery cell.

[0047] The voltage difference between cells (V t,k - V t,k-1), which is calculated in equation 2, can be derived from voltage changes due to internal resistance, voltage changes due to capacitance and hysteresis effects of the RC circuit.

[0048] The voltage change due to internal resistance can be expressed using the term R. s (I k - I k-1 ) in equation 2. In this expression, R represents s represents the resistance of the cell and (I k - I k-1 ) represents a change in current. This reflects the voltage change caused by the influence of resistance in relation to the current change.

[0049] The voltage change due to capacity in the battery equivalent model can be expressed using the expression Δt / C1 (I k - I k-1) can be calculated. In this expression, C1 represents the capacitance of the capacitor in the battery equivalent model, and Δt represents a time interval. The expression representing the voltage change due to capacitance reflects the influence of the current change on the charging and discharging of the capacitor.

[0050] The hysteresis effect of the RC circuit can be expressed by the formula (1 - Δt / R1C1)(V L,k-1 -V L,k-2 ) can be calculated. In this expression, R1 and C1 represent a resistor and a capacitor, respectively, and the expression (1 - Δt / R1C1) represents the properties of the RC circuit over time. The expression (V L,k-1 - V L,k-2 ) represents the voltage change between two previous points, and this expression reflects the influence of the voltage change in a previous cell on the current state.

[0051] In one embodiment, if the voltage change is due to capacitance Δt / C1 (Ik - I k-1 When (1 - Δt / R1C1) approaches zero, it indicates that the voltage change due to the capacitor is negligible. When (1 - Δt / R1C1) approaches zero, it indicates that the time constant Δt / R1C1 is large, meaning that the system response is very fast or the state of the previous point has little influence on the current state.

[0052] If the accumulated driving distance exceeds a predetermined value, the driving distance reflection unit 133 can adjust a ratio of at least one of the calculated voltage change values ​​in the battery equivalent model, taking into account degradation differences between cells within the battery module. The adjusted calculated value can then be set as a threshold.

[0053] In one embodiment, the distance reflection unit 133 can calculate a voltage deviation variation (DDVD) criterion using equation 3. The voltage deviation variation (DDVD) criterion can be used to detect a battery anomaly based on voltage deviation variations between the battery cells. (Vmax,k−Vmin,k)−(Vmax,k−1−Vmin,k−1)=(Vt,k−Vt,k−1)×a%

[0054] Referring to equation 3, the difference between the maximum and minimum voltage at the current point (k) (V) represents max,k - V min,k This represents the voltage difference between the cells with the highest and lowest voltage in the battery pack. This value represents the voltage deviation at the current point and can be used as an indicator of imbalance between the cells.

[0055] The difference between the maximum and minimum voltage at the previous point (k-1), V max,k-1 - Vmin,k-1 represents the stress deviation at the previous point. In one embodiment, the stress deviation at the current point is compared with that at the previous point to evaluate how the stress deviation has changed.

[0056] V t,k - V t,k-1This expression represents the voltage change calculated based on the equivalent circuit model (ECM) and indicates the voltage change between the current point and the previous point, estimated from the ECM. This expression is used to establish a reference for the actual voltage deviation variation based on the voltage change obtained from the equivalent model. The percentage factor a% adjusts the voltage change by a specific ratio to establish the voltage deviation variation (DDVD) criterion. In one embodiment, the percentage factor a% is preset according to the system design, and the sensitivity to voltage deviation variations can be adjusted.

[0057] In one embodiment, the route reflection unit 133 uses equation 3 to evaluate the DDVD threshold by the following process.

[0058] First, the route reflection unit 133 calculates the voltage deviation variation (V max,k - V min,k ) - (V max,k-1 - V min,k-1 ) between the current point and the previous point. This value represents how the imbalance between the cells changes over time. Next, the voltage change of each cell is multiplied by a percentage factor to adjust its contribution to the overall voltage variation. This reflects the impact of a specific cell's voltage change on the overall voltage variation of the battery pack. If the calculated value exceeds a predetermined threshold, the system can determine that an anomaly exists in the battery cell or battery pack and issue a warning. In one embodiment, Equation 3 can be used to monitor the voltage imbalance between the battery cells in real time.

[0059] Additionally, in one embodiment, the driving distance reflection unit 133 compares the voltage deviation variation criterion calculated using Equation 3 with the driving results of a normal battery and detects signs of battery anomaly based on the comparison results. For example, the driving distance reflection unit 133 can determine that the battery is anomalous if the driving result value exceeds the voltage deviation variation criterion, which is adjusted in real time during driving. In one embodiment, the voltage deviation variation criterion repeatedly increases and decreases according to the magnitude of the current. In this case, the voltage deviation variation of a normal battery has a shape similar to the threshold value and can be calculated as a ratio greater than or equal to a predetermined level that does not exceed the criterion.

[0060] Fig. Figure 4 is a diagram showing test results that analyze the voltage variation of a battery using the Voltage Variation (DDVD) criterion according to an embodiment of the present disclosure.

[0061] Fig. 4(B) is a diagram showing the DDVD criterion for voltage changes calculated in the embodiment, and Fig. Figure 4(A) is an enlarged diagram illustrating the change in the DDVD criterion for voltage changes occurring at a given time.

[0062] With reference to Fig. 4(A) the peaks in the upper diagram correspond to the voltage changes in the lower diagram, indicating that the voltage deviation variation (DDVD) criterion adequately detects such changes.

[0063] With renewed reference to Fig. 4(A) The green graph above visually represents the Voltage Variation (DDVD) criterion, which serves as a reference value for detecting an anomaly based on the voltage variation calculated at a specific time. Each peak in the graph represents a voltage change occurring over time and indicates the moment when an anomaly is determined according to the Voltage Variation (DDVD) criterion.

[0064] The lower diagram shows the voltage variation monitored in the battery. This illustrates how the voltage difference between battery cells changes over time. Voltage variations can occur due to changes in cell states, load, or temperature, and they serve as an important factor in detecting abnormal conditions.

[0065] In one embodiment, the battery anomaly detection device sets the minimum threshold for voltage anomaly determination to 3 mV. This value is determined using conventional diagnostic trouble code (DTC) criteria and voltage detection accuracy. In this embodiment, if the voltage deviation variation is 3 mV or greater, it can be considered an abnormal condition. Because the voltage deviation variation criterion incorporates voltage deviation variations, it is sensitive to voltage changes and enables anomaly detection.

[0066] The diagram of the voltage deviation variation criterion is similar in form to the voltage deviation variation of the monitored battery. This is because the voltage deviation variation criterion accurately reflects actual voltage change patterns. This indicates that the embodiment can effectively perform anomaly detection corresponding to voltage changes.

[0067] Fig. Figure 4 visually demonstrates how effectively the Voltage Variation Deviation (DDVD) criterion responds to actual voltage changes. It also shows how the DDVD criterion detects voltage deviations of 3 mV or more and identifies abnormal conditions.

[0068] Fig. 5 is a diagram illustrating the voltage deviation variation criterion and the deviation change of a supervised equivalent circuit model (ECM) according to an embodiment of the present disclosure.

[0069] With reference to Fig. 5 represents “a” the ECM voltage variation, which allows identification of the ECM voltage variability over time, and “b” represents the voltage variation criterion. Fig. Figure 5 illustrates the times at which anomalies are detected by comparing the ECM voltage deviation variation. In one embodiment, a voltage change exceeding a predetermined threshold can be considered an anomalous condition.

[0070] The following describes a battery anomaly detection method of the present disclosure with reference to Fig. 6 described. The battery anomaly detection method using a Fig. The voltage deviation variation shown in Figure 6 can be performed by a battery anomaly detection device 100 which uses a voltage deviation variation and includes the processor 130.

[0071] Meanwhile, Fig. 6 is merely illustrative, and the scope of the present disclosure is not limited to those shown therein. For example, the steps can be carried out in a sequence that differs from that shown in Fig. The 6 shown differ, at least one of which is in Fig. The 6 steps shown cannot be performed, or one or more additional steps are required. Fig. Further steps that are not shown in section 6 can be carried out.

[0072] The following section describes in sequence the method for detecting a battery anomaly using voltage variation. The operation (function) of the method according to the embodiment is essentially the same as that of the system, and therefore repetitive descriptions are omitted. Fig. 1 to Fig. 5 omitted.

[0073] Fig. Figure 6 is a flowchart illustrating the method for detecting a battery anomaly using a voltage variation according to an embodiment of the present disclosure.

[0074] With reference to Fig. Step 6 (S100) calculates the battery voltage change and stores it as a variable. Step 200 calculates the voltage variation (DDVD), which is the change in the voltage difference between individual cells within a battery module. Step 300 detects a battery anomaly using the voltage variation (DDVD) and the stored variable.

[0075] According to the problem-solving method described above in the present disclosure, the effect of enabling adaptation to different operating conditions and changes in battery state can be provided by dynamically adjusting the threshold for determining a battery anomaly based on the current change.

[0076] Furthermore, according to the problem-solving method described above in the present disclosure, anomaly detection errors can be reduced by adjusting the voltage change, which serves as the criterion for anomaly determination, as a variable using a battery equivalent model instead of a constant, thereby reflecting current usage patterns.

[0077] Additionally, according to the problem-solving method described above in the present disclosure, by managing the battery based on the voltage deviation variation instead of the voltage change, anomaly detection errors due to parameter errors in the battery equivalent model can be further reduced.

[0078] Furthermore, according to the problem-solving method described above in the present disclosure, the normal battery state can be effectively determined under various conditions, such as rapid charging or high-performance discharging.

[0079] Furthermore, according to the problem-solving method described above in the present disclosure, false positives and false negatives can be reduced by using a variable threshold instead of a fixed threshold, thereby enabling more accurate detection of actual anomalous signs of the battery.

[0080] Additionally, according to the problem-solving method described above in this disclosure, by using a battery equivalent model that reflects the physical and chemical properties of the battery, it is possible to predict changes in voltage, internal resistance, and other variables associated with current variation more accurately. This provides a deeper understanding than simple voltage change detection and allows for a more detailed assessment of the battery's state of health.

[0081] Furthermore, according to the problem-solving method described above in this disclosure, anomaly detection based on the equivalent model enables proactive prediction of and response to problems before the battery reaches a critical state. This can contribute to extended cycle life and stable battery operation.

[0082] Furthermore, according to the problem-solving method described above in the present disclosure, since the parameters of the battery equivalent model may vary depending on battery degradation or changes in ambient temperature, the effect of reducing errors in the calculated threshold can be achieved by taking these variations into account.

[0083] Additionally, according to the problem-solving method described above in the present disclosure, by monitoring deviations between multiple cells within the battery module and managing relative changes, it is possible to minimize the influence of individual cell parameter variations on the overall anomaly detection.

[0084] Furthermore, according to the problem-solving method described above in the present disclosure, by detecting anomalies based on relative changes between cells instead of absolute values, the influence of parameter errors can be reduced, thereby enabling more stable anomaly detection.

[0085] Furthermore, according to the problem-solving method described above in this disclosure, by reflecting battery properties that vary depending on environmental conditions (such as temperature and humidity), it is possible to maintain high reliability even under different environmental conditions. This ensures that battery reliability can be maintained even in electric vehicles, drones, and outdoor devices that are subject to significant environmental fluctuations.

[0086] Additionally, according to the problem-solving method described above in this disclosure, anomalous signs can be detected at an early stage by performing current-change-based detection, before incidents such as fires occur, thus enabling timely and appropriate response measures. Furthermore, according to the problem-solving method described above in this disclosure, unnecessary maintenance costs can be reduced by accurately identifying the battery state and detecting anomalous signs in advance. This contributes to a reduction in overall operating costs and enables the efficient use of resources.

[0087] The disclosed subject matter is merely illustrative, and various modifications and implementations can be made by a person skilled in the art without deviating from the spirit and scope of the claims. Accordingly, the scope of protection of the present disclosure is not limited to the specific embodiments described above.

Claims

[1] Device for detecting a battery anomaly using a voltage variation, the device comprising: a memory adapted to store at least one instruction for detecting a battery anomaly using a voltage variation; and a processor adapted to perform an operation according to the instruction, where the processor is adapted as follows: Calculating a voltage change in the battery and storing the calculated voltage change as a variable; and Calculating a differential deviation voltage detection (DDVD), which is a change in the voltage difference between respective cells within a battery module, and detecting a battery anomaly using the differential deviation voltage detection (DDVD) and the stored variables. [2] Device according to claim 1, wherein the processor comprises a battery equivalent model voltage change calculation unit adapted to calculate a voltage change of the battery using parameters relating to a state of charge (SOC) of the battery when a current is applied to an equivalent circuit model (ECM). [3] Device according to claim 2, wherein the processor comprises a distance reflection unit adapted to detect when an accumulated distance traveled exceeds a predetermined value, to adjust the ratio of at least one of the calculated values ​​of the voltage change in the battery equivalent model by taking into account deterioration differences between cells within the battery module and to set the adjusted calculated value as a voltage deviation variation (DDVD) criterion. [4] Device according to claim 2 or 3, wherein the parameters relating to the state of charge (SOC) are a voltage change (Δt / C1 (I k - I k-1 )) due to the capacity of the battery equivalent model and circuit characteristics (1 - Δt / R1C1) of an RC circuit. [5] Device according to claim 3, wherein the driving distance reflection unit is adapted to compare the calculated voltage deviation variation criterion with driving results of a normal battery and to adjust an increase or decrease of the criterion according to a current level. [6] Method for detecting a battery anomaly using a voltage variation, the method comprising: Calculating a voltage change in the battery and storing the calculated voltage change as a variable; and Calculating a differential deviation voltage detection (DDVD), which is a change in the voltage difference between respective cells within a battery module, and detecting a battery anomaly using the differential deviation voltage detection (DDVD) and the stored variables. [7] Method according to claim 6, wherein the step of detecting a battery anomaly comprises calculating, by a battery equivalent model voltage change calculation unit, a voltage change of the battery using parameters relating to a state of charge (SOC) of the battery when a current is applied to an equivalent circuit model (ECM). [8] Method according to claim 7, wherein the step of calculating a voltage change of the battery comprises: If an accumulated driving distance exceeds a predetermined value, adjust, by means of a driving distance reflection unit, a ratio of at least one of the calculated values ​​of the voltage change in the battery equivalent model by taking into account deterioration differences between cells within the battery module; and set the adjusted calculated value as a voltage deviation variation (DDVD) criterion. [9] Method according to claim 7 or 8, wherein the parameters relating to the state of charge (SOC) are a voltage change (Δt / C1 (I k - I k-1 )) due to the capacity of the battery equivalent model and circuit characteristics (1 - Δt / R1C1) of an RC circuit. [10] Method according to claim 8, wherein the step of setting the adjusted calculated value as a voltage deviation variation (DDVD) criterion comprises the following: Comparing the calculated voltage deviation variation criterion with driving results of a normal battery; and adjusting an increase or decrease of the criterion according to a current level.

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