AI-Based Fault Prediction and Response System for Lithium-Ion Battery Modules

Abstract

The present invention relates to an artificial intelligence-based safety management system for predicting and responding to failures in lithium-ion battery modules. It collects battery status data, such as temperature, current, voltage, and impedance, in real time to predict the probability and timing of failure occurrence through a deep learning model, and performs charging restriction, current bypass, and circuit interruption in stages based on the reliability of the prediction results. The control module determines a risk level based on this prediction information, suppresses transient voltage and surge current by controlling the MOSFET switching circuit and protection circuit of the automatic cutoff module, and stably restores the circuit through a reversible control loop when the abnormal state is resolved. Furthermore, the present invention includes a self-correcting safety structure configured to immediately block or bypass emergency situations, such as sudden temperature rises or sudden current fluctuations, without AI computation by adding a reflective emergency control layer that operates prior to the AI ​​prediction judgment stage, and in which the AI ​​learns the operation history to correct malfunction patterns. Through this, prediction-based control and physical reflective control cooperate in parallel, and by adapting to environmental changes or sensor noise and self-correcting sensitivity and threshold values ​​even during long-term operation, high stability and continuous operation capabilities can be maintained even in high-output and fast-charging environments.

Description

Technology Field

[0001] The present invention relates to technology concerning the safe operation and lifespan management of lithium-ion battery modules, and in particular, to an AI-based fault prediction and response system that predicts battery failures such as thermal runaway, short circuit, and capacity degradation based on state data such as temperature, voltage, current, and impedance using artificial intelligence (AI), and performs charge limiting, current bypass, and circuit interruption in stages according to the prediction results.

[0002] More specifically, the invention relates to a Dual Safety Control Architecture that minimizes accidents caused by failure prediction delays and improves the reliability of a battery module by including a control logic that estimates the probability and timing of failure occurrence using a deep learning prediction model that analyzes battery status data collected from a sensor network in real time and controls a current cutoff module according to the prediction result, and a Reflex Safety Layer that performs hardware cutoff immediately before AI judgment.

[0003] In addition, the present invention can be applied to the field of AI-integrated battery diagnosis and protection technology for maintaining the safety and efficiency of lithium-ion battery packs in battery management systems (BMS), electric vehicles (EV, HEV, PHEV), industrial heavy equipment, energy storage systems (ESS), or high-power power systems, etc. Background Technology

[65535] Lithium-ion batteries are widely used in electric vehicles (EVs), hybrid electric vehicles (HEVs), energy storage systems (ESS), industrial heavy equipment, drones, and various electronic devices due to their high energy density, excellent charge / discharge efficiency, and long lifespan. Meanwhile, due to the nature of the high-energy chemical systems, lithium-ion batteries are susceptible to abnormalities such as thermal runaway, internal short circuits, overcharge, and overdischarge caused by heat generation, voltage deviations, current concentration, cell imbalances, and aging during the charging and discharging process. These abnormalities can spread to adjacent cells or modules, potentially leading to fire, explosion, or failure of the battery pack. Conventionally, Battery Management Systems (BMS) have been used to manage these risks. A typical BMS protects the battery by measuring physical quantities such as temperature, voltage, and current, determining whether the measured values ​​exceed preset reference values ​​or thresholds, and then limiting or cutting off charging or activating cooling devices. In addition, some systems are configured to use hardware protection circuits, such as fuses, relays, and short-circuit switches, to interrupt the current path in the event of overcurrent or abnormal voltage. However, since these conventional technologies generally rely on comparing measured values ​​with fixed threshold values, they have limitations in identifying complex abnormal signs early on, such as degradation trends over time, the accumulation of imbalances between cells, and correlations between multiple state variables. In other words, because conventional BMSs generally have a reactive structure that detects and responds to abnormalities after they occur, it is difficult to predict potential risks prior to failure. Furthermore, in conventional centralized control structures, data transmission and computational delays may occur during the process of collecting sensor data, analyzing it in the controller, and generating cutoff signals.In particular, in large battery packs composed of multiple cells and modules, such delays can accumulate, and if a sudden temperature rise or overcurrent occurs during this time, actual physical damage may already have occurred. Therefore, there are cases where it is difficult to respond immediately based solely on the judgment of software or a higher-level controller. Meanwhile, there are also issues when hardware protection circuits are used in parallel. For instance, shutdown may be triggered by sensor noise, transient current spikes, or environmental disturbances even when there is no actual failure; in such cases, the system may stop unnecessarily or require manual recovery, thereby reducing availability. Conversely, if the threshold values ​​of the protection circuit are set too conservatively, there is a risk that the response to actual abnormal situations may be delayed. Furthermore, the internal resistance, capacity, and voltage balance of battery cells vary depending on the usage period, charge and discharge cycles, and operating environment. However, conventional fixed threshold-based protection methods fail to adequately reflect these aging characteristics; consequently, the error between the actual state and the judgment criteria increases over time, which can lead to a decrease in diagnostic accuracy. To address these limitations, technologies utilizing artificial intelligence (AI) or machine learning techniques to analyze time-series data such as temperature, voltage, current, and impedance to predict signs of battery abnormalities or the likelihood of failure have recently been proposed. For instance, methods to estimate the probability of failure or the predicted timing using time-series analysis models such as LSTM, GRU, and CNN are being studied. However, these AI-based diagnostic technologies also require a certain amount of time for prediction computation, and their reliability can vary depending on the quality of input data, the sufficiency of training data, and changes in the operating environment; thus, there are limitations to applying them as safety control structures directly coupled with actual hardware control. In particular, thermal runaway or overcurrent may occur while waiting for the AI's judgment results, while conversely, if the hardware protection circuit operates overly sensitively, unnecessary shutdowns may occur even due to transient noise.In other words, conventional technology has not sufficiently resolved the issues of how to harmoniously combine predictive diagnosis using battery status data with physical protection control capable of immediate shutdown, and how to adjust protection standards in response to environmental changes and aging. Therefore, a new battery protection technology is required that can prevent accidents caused by prediction delays while reducing excessive malfunctions by operating predictive control—which analyzes battery status data in real time to predict the likelihood of failure—in parallel with physical reflective control capable of immediate operation even before a prediction judgment is made. The problem to be solved

[0024] The present invention aims to overcome the structural limitations of the aforementioned conventional technology by providing a dual safety control structure capable of predicting various signs of failure that may occur during the operation of a lithium-ion battery module earlier and more reliably, performing stepwise responses according to risk levels based on the prediction results, and performing immediate shutdown at the hardware level without waiting for a judgment from artificial intelligence.

[0025] That is, the present invention analyzes various state data such as temperature, voltage, current, impedance, cell balancing voltage difference, and internal resistance fluctuations in a time series to detect potential patterns of abnormal states such as thermal runaway, internal short circuit, and rapid degradation at an early stage, and performs multi-stage safety controls such as charging limiting, current bypassing, and sequential shutdown according to the predicted failure probability and prediction reliability, thereby minimizing unnecessary system shutdown, lifespan reduction, and efficiency loss associated with the single and batch shutdown methods of the conventional technology.

[0026] In addition, the collected sensor data is simultaneously provided to an artificial intelligence diagnostic module and a hardware reflection control circuit. Even before the AI ​​judgment is completed, if a dangerous situation requiring immediate action, such as a sudden rise in temperature, a sudden change in current, or abnormal voltage, is detected, the reflection control layer performs an ultra-high-speed emergency control function by prioritizing the interruption or diversion of the current. This effectively prevents initial accidents that may occur due to delays in predictive computation or model uncertainty, and can dramatically improve the system's response speed to signs of failure.

[0027] Furthermore, the control module integrates AI diagnostic results and reflective control operation history to dynamically adjust control policies by risk level, and performs a reversible control loop that automatically reconnects blocked circuits when abnormal conditions are resolved, thereby maintaining the continuous operation and availability of the battery system.

[0028] In particular, the present invention is designed so that artificial intelligence can learn and accumulate the operation history of the reflection control layer, thereby enabling it to self-analyze and correct malfunction patterns caused by sensor noise or environmental factors. Accordingly, the sensitivity threshold, debounce time, return delay, and multi-sensor cross-validation conditions of the reflection control circuit are corrected in real-time in an actual operating environment, thereby minimizing overreaction or unnecessary blocking and maintaining response reliability over the long term.

[0029] In addition, electronic relays or MOSFET switching elements are applied to the automatic cutoff module, and protection elements such as transient voltage suppressors (TVS), RC snubbers, and gate soft-start circuits are included to suppress transient voltage and surge currents occurring during switching, thereby reducing voltage spikes and current shocks at the moment of switching. This reduces the risk of damage to electronic components and ensures stable cutoff operation even in high-speed control environments.

[0030] Ultimately, the present invention aims to realize a battery protection technology that maintains high reliability even during long-term operation by implementing a hybrid safety control structure in which AI-based predictive control and reflexive emergency control cooperate in parallel, thereby simultaneously securing the accuracy of fault prediction and the speed of response, and self-correcting malfunctions of reflexive control through AI learning.

[0031] Through this, the present invention overcomes the limitations of existing threshold-based monitoring or single AI prediction methods and provides a new type of battery safety management technology that intelligently integrates and controls the entire safety management process from fault prediction to accident response to circuit recovery. means of solving the problem

[0033] A fault prediction and response system for a lithium-ion battery module according to one embodiment of the present invention discloses an AI-based fault prediction and response system for a lithium-ion battery module, comprising: a data collection module that measures battery status data including temperature, voltage, current, and impedance at a predetermined period and provides it to a central processing unit; an artificial intelligence diagnostic module that analyzes the status data provided by the data collection module to learn abnormal signs over time and calculates the probability of failure and the expected time of failure; a control module that determines a risk level in stages using the prediction result of the artificial intelligence diagnostic module as input and generates commands for charging restriction, current bypass, or circuit interruption according to the level; and an automatic shut-off module that disconnects the corresponding battery cell from the circuit or forms a bypass path in response to a command from the control module, wherein the control module is configured to perform safety control in the order of a restricted charging stage, a partial bypass stage, and a complete shut-off stage by controlling the automatic shut-off module in stages when the predicted probability of failure satisfies a threshold range that is pre-set or corrected during operation.

[0034] Another embodiment of the present invention discloses an AI-based fault prediction and response system for a lithium-ion battery module, wherein the AI ​​diagnostic module uses temperature, voltage, current, impedance, cell balancing voltage difference, charge / discharge history, and internal resistance variation as input features, and uses a time-series predictive deep learning model to predict signs of thermal runaway, short circuit, or capacity degradation at an early stage, and self-relearns model parameters using state data accumulated during operation and actual fault history, and automatically corrects the criteria for calculating prediction accuracy and reliability according to environmental and aging conditions.

[0035] According to one embodiment of the present invention, an AI-based fault prediction and response system for a lithium-ion battery module is disclosed, wherein the control module receives a predicted fault time and a prediction reliability index together from the artificial intelligence diagnostic module, and prioritizes a complete shutdown command when the prediction reliability index is above an upper threshold, and a charging limit or cooling control when it is below a lower threshold, and monitors the operating status of the automatic shutdown module to perform a reversible control loop that reconnects the cell to the circuit when the abnormal state is resolved or the prediction result returns to a normal range.

[0036] According to another embodiment of the present invention, the automatic cutoff module includes an electronic relay or MOSFET switching element, and further includes a transient voltage suppressor (TVS), an RC snubber circuit, or a gate soft start circuit for absorbing transient voltages occurring during switch switching, thereby configuring to suppress surge currents and voltage spikes during cutoff, thereby disclosing an AI-based fault prediction and response system for a lithium-ion battery module.

[0037] In an AI-based fault prediction and response system for a lithium-ion battery module according to one embodiment of the present invention, the system further comprises a reflex safety layer in which, prior to the prediction judgment procedure of the artificial intelligence diagnostic module, when a sudden temperature rise, a sudden change in current, or a voltage abnormality is detected within a predetermined judgment time window, a physically independent hardware reflex control circuit immediately cuts off or diverts the current without waiting for the artificial intelligence judgment, thereby enabling the AI-based prediction control and physical reflex control to operate in parallel to minimize the occurrence of accidents caused by prediction delays.

[0038] In addition, according to one embodiment of the present invention, the artificial intelligence diagnostic module collects and analyzes the operation history of the reflection control layer to learn malfunction patterns and performs correction logic, and the control module sets at least one of the sensitivity threshold of the reflection control circuit, debounce time, return delay, and multi-sensor cross-check condition as a parameter adjustable during operation by reflecting the correction result, thereby disclosing an AI-based fault prediction and response system for a lithium-ion battery module configured to prevent overreaction, excessive blocking, or delayed return of the reflection control. Effects of the invention

[0039] According to the present invention, by monitoring the operating status of a lithium-ion battery module in real time and enabling artificial intelligence predictive control and reflective emergency control to operate in parallel, the problems of prediction delay and malfunction that occurred in a conventional single control loop can be fundamentally resolved.

[0040] In other words, sensor data is simultaneously transmitted to the AI ​​diagnostic module and the reflection control circuit, allowing for simultaneous stepwise response based on AI judgment and physical circuit interruption. This enables immediate safety control without delay, even in sudden situations such as rapid temperature rises or current spikes.

[0041] In addition, the artificial intelligence diagnostic module of the present invention calculates the probability of failure and the predicted time of failure using a time-series prediction deep learning model, so it can precisely analyze subtle abnormal trends or deterioration patterns over time that are difficult to identify with conventional threshold comparison methods.

[0042] This allows for the early detection of potential failures in battery modules, thereby preventing accidents such as thermal runaway, short circuits, and capacity degradation, and enables predictive preventive control rather than reactive control after a failure occurs.

[0043] The control module determines risk levels in stages based on prediction results and distinguishes responses such as charge limiting, current bypassing, and complete shutdown, thereby preventing unnecessary total shutdowns or system interruptions. Furthermore, by performing a reversible control loop that automatically restores the circuit once the abnormal condition is resolved, it can maintain the continuous operation and availability of the battery system. This is particularly useful in application environments where long-term stable operation is essential, such as industrial heavy equipment or energy storage systems.

[0044] The automatic cutoff module includes a TVS diode, RC snubber, gate soft start circuit, etc., along with a MOSFET or electronic relay, to effectively suppress transient voltage and surge current occurring at the time of switching, thereby preventing damage to the power converter or adjacent circuits and ensuring the stability and durability of the circuit even in a high-speed control environment.

[0045] In addition, an important feature of the present invention is that the artificial intelligence diagnostic module learns the operational history data of the reflection control layer to identify malfunction patterns on its own and adjusts parameters such as the sensitivity threshold, debounce time, return delay, and multi-sensor cross-checking conditions of the reflection control circuit in real time. Through this, overreaction or unnecessary blocking of the reflection control circuit can be reduced, and the balance between AI prediction and hardware control can be dynamically maintained.

[0046] As a result, the system performs adaptive self-correcting control functions by repeatedly learning and correcting itself even during operation, continuously improving stability and reliability even during long-term operation.

[0047] As such, the present invention provides a dual safety control structure that combines the precision of AI-based predictive control with the immediacy of hardware reflective control, thereby improving fault prediction accuracy, response speed, control stability, and system reliability.

[0048] Therefore, the present invention is applicable not only to lithium-ion battery modules but also to high-output, high-density power systems such as electric vehicles, energy storage devices, industrial robots, and drones, and possesses high industrial utility value and scalability as an AI-integrated next-generation battery management technology. Brief explanation of the drawing

[0049] FIG. 1 is a flowchart illustrating a fault prediction-based classification and control policy determination procedure according to one embodiment of the present invention. FIG. 2 is a block diagram showing the overall system configuration and signal flow between modules according to one embodiment of the present invention. FIG. 3 is a block diagram illustrating the internal circuit configuration of an automatic cutoff module according to one embodiment of the present invention. FIG. 4 is a block diagram showing the data flow between a state storage unit and each module according to an embodiment of the present invention. Specific details for implementing the invention

[0050] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present invention. However, it is evident that these embodiments can be practiced without such specific descriptions.

[0051] As used herein, terms such as “component,” “module,” “system,” etc. refer to computer-related entities, hardware, firmware, software, combinations of software and hardware, or executions of software. For example, a component may be, but is not limited to, a procedure, processor, object, execution thread, program, and / or computer running on a processor. For example, both an application running on an electronic device and the electronic device itself may be a component. One or more components may reside within a processor and / or execution thread. A component may be localized within a single computer. A component may be distributed among two or more computers. Additionally, these components may be executed from various computer-readable media having various data structures stored therein. Components may communicate through local and / or remote processes, for example, according to signals having one or more data packets (e.g., data from a component interacting with another component in a local system or distributed system, and / or data transmitted through signals to other systems and networks such as the Internet).

[0052] Furthermore, the term "or" is intended to mean an implicit "or" rather than an exclusive "or." That is, unless otherwise specified or evident from the context, "X uses A or B" is intended to mean one of the natural implicit substitutions. In other words, if X uses A; if X uses B; or if X uses both A and B, "X uses A or B" may apply to any of these cases. Additionally, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the enumerated related items.

[0053] Additionally, the terms “comprising” and / or “comprising” should be understood to mean that such features and / or components are present. However, the terms “comprising” and / or “comprising” should be understood not to exclude the presence or addition of one or more other features, components and / or groups thereof. Furthermore, unless otherwise specified or clearly evident from the context to indicate a singular form, the singular in this specification and claims should generally be interpreted to mean “one or more.”

[0054] And, the term "at least one of A or B" should be interpreted to mean "a case including only A," "a case including only B," or "a combination of A and B."

[0055] Those skilled in the art should recognize that the various exemplary logical blocks, configurations, modules, circuits, means, logics, and algorithmic steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly exemplify the interchangeability of hardware and software, various exemplary components, blocks, configurations, means, logics, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented in hardware or software depends on the specific application and design constraints imposed on the overall system. Skilled technicians may implement the described functionality in various ways for each specific application. However, such decisions regarding implementation should not be construed as moving out of the scope of this disclosure.

[0056] In this document, network functions, artificial neural networks, and neural networks can be used interchangeably.

[0057] The various embodiments described herein may be implemented, for example, in recording media and storage media readable by a computer or similar device using software, hardware, or a combination thereof.

[0058] According to hardware implementation, the embodiments described herein may be implemented using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, and other electrical units for performing functions. In some cases, the embodiments described herein may be implemented as the processor itself of an electronic device.

[0059] FIG. 1 is a block diagram illustrating the overall configuration and signal flow of an AI-based fault prediction and response system (1000) for a lithium-ion battery module according to one embodiment of the present invention.

[0060] First, when the predicted value calculated by the AI ​​diagnostic module is input, the system determines a risk level classified as high, medium, or low based on each prediction probability. Control policies are applied differently depending on the determined level; a complete shutdown command is issued for high risk, charging restrictions or cooling control for medium risk, and a normal operation policy is selected for low risk. The selected control policy is transmitted to the automatic shutdown module via the control module, and the operating status is continuously monitored thereafter; if an anomaly occurs, the system returns to the prediction procedure and reconnects.

[0061] FIG. 2 is a block diagram showing the overall system configuration and signal flow between modules according to one embodiment of the present invention.

[0062] The data collection module collects temperature, voltage, and current information from the battery pack and cells in real time and transmits it to the AI ​​diagnostic module. The AI ​​diagnostic module analyzes the input data to calculate the probability of failure and risk level, and provides this information to the control module. The reflective emergency control layer is directly connected to the data collection module and operates independently at the hardware level when immediate shutdown without a prediction procedure is required. The control module integrates AI prediction information and reflective control signals to control the automatic shutdown module or the cooling control module, thereby performing safety control for the battery pack, module, and cell structures.

[0063] An embodiment of the present invention is described based on FIGS. 1 and FIGS. 2.

[0064] The system (1000) is composed of a battery pack / module / cell structure (800) as the center, a data collection module (100) that measures the state therein in real time, an artificial intelligence diagnostic module (200) that analyzes collected time-series data to predict the probability and timing of failure occurrence, a reflexive emergency control layer (500) that immediately responds to signs of physical abnormalities, a control module (300) that generates step-by-step safety control commands by synthesizing prediction results and reflexive control results, an automatic cutoff module (400) that cuts off or diverts current according to control commands, and a cooling control module (900) that controls the flow rate and distribution of cooling channels according to predicted risk levels and actual temperature distributions. These modules are interconnected via wired or wireless communication networks and integrally perform the entire process from battery system failure prediction, accident response, thermal management, to circuit recovery.

[0065] The data collection module (100) includes or is connected to temperature sensors, current sensors, voltage sensors, impedance measurement circuits, cell balancing voltage difference measurement units, internal resistance measurement units, etc. installed in a battery cell or cell group, and acquires measurement values ​​from each sensor at a predetermined period or whenever an event occurs. Timestamps are assigned to the collected sensor data so that temperature, current, voltage, and impedance values ​​occurring at the same time are aligned into a single sequence, and this aligned time-series data is provided in parallel to the artificial intelligence diagnostic module (200) and the reflective emergency control layer (500). The communication method can be configured in various ways, such as CAN, SPI, UART, Ethernet, or wireless link, and the data collection module (100) can also perform preprocessing such as filtering, unit conversion, and removal of outliers for each sensor signal. Through this, the physical state of each battery cell is monitored in real time, and reliable source data required for subsequent prediction and control calculations is secured.

[0066] The artificial intelligence diagnostic module (200) uses time-series state data input from the data collection module (100) as input features to detect early signs of failure such as thermal runaway, internal short circuit, rapid capacity degradation, and abnormal increase in internal resistance that may occur in the battery system, and performs the function of predicting the probability of failure and the expected time of occurrence. In this embodiment, the diagnostic module (200) may include one or more deep learning models suitable for time-series prediction, such as RNN, LSTM, GRU, Temporal CNN, and Transformer, and inputs not only temperature, voltage, current, and impedance, but also cell balancing voltage difference, charge / discharge history, SOH (State of Health) per cell, internal resistance variation, and typical patterns of past failures as multidimensional vectors.

[0067] The artificial intelligence diagnostic module (200) optimizes the model parameters using past operation history and actual failure time data during the learning phase, and continuously improves prediction accuracy and reliability by performing self-learning or periodic retraining using newly accumulated data and the operation history of the reflective emergency control layer (500) during operation.

[0068] The diagnostic module (200) calculates and provides to the control module (300) a reliability index reflecting the probability of failure occurrence, the estimated time of failure, the classification result of the type of failure, and the uncertainty of the prediction result as a prediction result, and also uses the blocking / bypass operation log transmitted from the reflective emergency control layer (500) so that the malfunction pattern of the reflective control is learned.

[0069] The reflective emergency control layer (500) is a hardware-centric emergency control layer that evaluates sensor data directly input from the data collection module (100) in real time within a short judgment time window (e.g., several ms to tens of ms) without waiting for the failure prediction result of the artificial intelligence diagnostic module (200), and determines whether immediate safety measures are required.

[0070] When the reflective emergency control layer (500) detects a rapid rise event in which the temperature rise rate exceeds a predetermined threshold, an overcurrent spike occurring for a very short period of time, an event in which the voltage momentarily deviates significantly from the allowable range, or an abnormal pattern presumed to be a sensor open circuit or short circuit, it directly outputs a blocking or bypass command to the automatic blocking module (400) without waiting for a judgment from the artificial intelligence diagnostic module (200). In addition, if it is determined that there is a high risk of failure, it transmits an emergency cooling request signal to the cooling control module (900) to rapidly remove heat in the early stages of thermal runaway by increasing the flow rate of the main pump and opening the branch path.

[0071] Information regarding the operation history of the reflective emergency control layer (500), such as how many times blocking / bypassing occurred under certain sensor conditions, whether a failure actually occurred after the event, and whether there were any blocking cases judged to be oversensitive, is fed back to the artificial intelligence diagnostic module (200) and the control module (300). Through this, the AI ​​model learns the malfunction patterns of the reflective control and corrects its judgment criteria, and the control module (300) can dynamically adjust the sensitivity threshold, debounce time, return delay, multi-sensor cross-checking conditions of the reflective control layer (500).

[0072] The control module (300) determines the risk level of a battery cell or cell group in stages by combining the failure probability, expected failure time, failure type classification result, and prediction reliability index transmitted from the artificial intelligence diagnostic module (200), and the emergency cutoff event and physical abnormality sign information transmitted from the reflective emergency control layer (500). The risk level can be divided into stages such as a normal state, a preliminary warning state, a warning state, and an emergency state, and corresponding to each stage, the control module (300) generates different control commands for the automatic cutoff module (400) and the cooling control module (900).

[0073] For example, in the preliminary warning stage, gradual control is performed to limit the charging current to a certain ratio and increase the cooling flow rate for some modules, and in the warning stage, intensive cooling is performed for the corresponding modules along with current bypass or blocking of some cells. If determined to be an emergency stage, a command for total blocking or blocking by cell group is output to the automatic blocking module (400), and at the same time, the cooling control module (900) is switched to a high-flow emergency cooling mode. In addition, the control module (300) periodically monitors the switch on / off state of the automatic blocking module (400), whether a bypass path is formed, whether the protection circuit is operating normally, the pump driving state of the cooling control module (900), and the opening state of the flow control valve, etc., to verify whether the command matches the actual physical operation, and performs additional protection measures if an abnormality is detected. When the abnormal state is resolved or the artificial intelligence diagnosis result returns to the normal range, the control module (300) performs a reversible control loop to sequentially reconnect the blocked cells to the circuit and gradually restore the cooling flow rate to the normal operating level to maintain the availability of the system.

[0074] The automatic cutoff module (400) is a core hardware component that cuts off or bypasses the battery current path in response to a control command from the control module (300) or the reflective emergency control layer (500).

[0075] In this embodiment, the automatic cutoff module (400) includes a MOSFET switching element (410), a gate driving unit (420) that controls the gate voltage, and a protection circuit unit (430) for suppressing transient voltage and surge current that may occur during the switching process.

[0076] The protection circuit (430) may be composed of one or more of a transient voltage suppressor (TVS) diode (431), an RC snubber circuit (432), and a gate soft start circuit (433), and absorbs and mitigates voltage spikes and current steps that occur at the moment the switching element (410) performs an on / off operation, thereby reducing thermal and electrical stress on the switching element and surrounding circuits. The automatic cutoff module (400) may be configured in a cell unit, a cell group unit, or a module unit, and can selectively cut off only specific cells or control only some modules to bypass and allow the remaining modules to continue operating according to a command from the control module (300). This enables rapid protection against localized failures while minimizing the shutdown of the entire system.

[0077] The cooling control module (900) performs the role of removing heat through a cooling channel placed in the battery pack / module / cell structure (800), and performs an active thermal management function in conjunction with the artificial intelligence diagnostic module (200), the reflective emergency control layer (500), and the control module (300).

[0078] The cooling control module (900) may be composed of a main pump that circulates cooling fluid throughout the battery pack, branched cooling paths that branch to each battery module or cell group, electronic flow control valves installed in each branched path, microfluidic channels formed on the cell surface or inside the module, and sensors that detect the temperature and flow rate of the cooling fluid inlet and outlet.

[0079] The control module (300) combines the failure probability and expected failure time calculated by the artificial intelligence diagnostic module (200), the temperature distribution per cell, and the rapid temperature rise event detected by the reflection control layer (500), and controls the driving speed of the main pump and the opening rate of each electronic flow control valve in stages. In the preliminary warning stage, the total flow rate is increased by a predetermined ratio, and preliminary cooling is performed by distributing more cooling flow to modules with large temperature deviations. If heat is concentrated only in a specific cell group, localized concentrated cooling is performed centered on the microfluidic channels connected to that cell group. When the reflection emergency control layer (500) detects an event judged to be a precursor to a rapid temperature rise or thermal runaway, the main pump is switched to a maximum flow rate mode by a command from the control module (300) or a separate emergency signal, and the electronic valves of all related branch flow paths are immediately opened to perform emergency cooling. The temperature difference and flow rate information of the cooling fluid inlet and outlet measured by the cooling control module (900) is fed back to the artificial intelligence diagnostic module (200), so that the correlation between thermal management performance and signs of failure is learned, and in the future, more precise judgments considering cooling conditions are possible when predicting failures.

[0080] In a normal operating state, the data collection module (100) periodically collects sensor data and provides it in parallel to the artificial intelligence diagnostic module (200) and the reflective emergency control layer (500), and the diagnostic module (200) determines the state as normal or low risk and notifies the control module (300). In this case, the control module (300) does not output a cutoff command to the automatic cutoff module (400) or maintains only a minimum charging limit, and the cooling control module (900) prioritizes efficiency by operating in a basic flow rate mode. When minute abnormal signs accumulate over a certain period and the artificial intelligence diagnostic module (200) calculates an increase in the probability of failure or an approach to the expected time, the control module (300) instructs a combination of charging limit, partial bypass, flow rate increase, and localized concentrated cooling according to the risk level. Meanwhile, if physical abnormalities such as a rapid temperature rise, overcurrent spikes, or abnormal voltage are detected within the judgment time window, the reflective emergency control layer (500) controls the switching element of the automatic cutoff module (400) without waiting for the artificial intelligence judgment to immediately cut off or divert the current, and simultaneously requests emergency cooling from the cooling control module (900) to suppress the progression of thermal runaway. Subsequently, the artificial intelligence diagnostic module (200) analyzes the operation history including the event and performs correction logic, and the control module (300) updates operational parameters such as sensitivity, debounce time, return delay, cross-check condition, and cooling flow rate distribution policy. When the abnormal state is resolved, the control module (300) safely reconnects the cell that was cut off to the circuit through a reversible control loop and restores the cooling system to a normal level, thereby simultaneously securing the availability and lifespan of the system.

[0081] In this way, the system (1000) of the present invention secures an immediate response capability unaffected by AI computation delay by configuring an artificial intelligence predictive control loop, a hardware reflective control loop, and an active cooling control loop in a parallel structure, and implements a self-correcting safety control structure in which the operation history of the reflective control and cooling control is continuously learned and corrected by an artificial intelligence diagnostic module (200) and a control module (300), thereby significantly improving safety, availability, and long-term reliability compared to a conventional single-loop-based battery management system.

[0082] In one embodiment of the present invention, the artificial intelligence diagnostic module analyzes battery status data periodically input from the data collection module to learn the pattern of change over time, reconstructs it into a hierarchical structure of cell unit, cell group unit, module unit, and pack unit, and performs the function of predicting the probability of failure and the expected time of failure for each layer. The data collection module basically includes temperature, voltage, current, and impedance, but also provides additional variables such as cell balancing voltage difference indicating voltage imbalance between cells, charge and discharge history, long-term and short-term fluctuations in internal resistance, state of charge (SOC) and state of health (SOH), ambient temperature, temperature of the inlet and outlet of the cooling channel, and vibration or external shock information. This data is synchronized at regular intervals along with identifiers for each cell, cell group, module, and pack and transmitted to the artificial intelligence diagnostic module.

[0083] The AI ​​diagnostic module receives this hierarchical time-series data as input and performs preprocessing to correct discontinuities caused by missing measurements, noise, sensor delays, and communication errors. This preprocessing may include smoothing using moving averages or exponential smoothing, noise removal using Kalman filters or wavelet transforms, normalization and scaling to standardize the units or ranges of values, removal of abnormal spikes through outlier detection, windowing by time interval, and aggregation by cell-cell group-module-pack hierarchy; through these steps, the data is transformed into a form suitable for the prediction model.

[0084] The artificial intelligence diagnostic module has a time series prediction structure based on deep learning and can apply one or more of the following: a recurrent neural network (RNN), a long short-term memory network (LSTM), a gated recurrent unit (GRU), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), and a transformer model including an attention mechanism. The type and depth of the model can be selected according to the system's computational resources, response time requirements, and battery application. For example, a relatively lightweight convolution-based structure can be used for mobile heavy equipment requiring high-speed response, while a transformer-based structure can be used for large energy storage devices where precise diagnosis and long-term prediction are important, allowing for simultaneous consideration of the correlations of the entire cell, module, and pack.

[0085] This model predicts the likelihood of the battery's condition transitioning from a normal range to an abnormal range by comprehensively analyzing not only the absolute values ​​of each sensor data but also the temporal rate of change, fluctuation patterns, temperature imbalances between cells and modules, temperature differences in cooling channels, charge / discharge cycles, and correlations with ambient temperature changes. The prediction results provide failure probability, expected time of failure, and classification of failure types (thermal runaway, internal short circuit, abnormal degradation, etc.) for each layer—cell, cell group, module, and pack—while simultaneously calculating an index value indicating the reliability of the prediction results.

[0086] In this invention, the prediction reliability index is used not merely as supplementary information, but as a key parameter for determining the control strategy. The artificial intelligence diagnostic module calculates reliability by synthesizing the model's output distribution, the degree of input data loss, whether retraining has been recent, and the degree of mismatch with the ambient temperature and cooling status, and classifies this into an upper threshold and a lower threshold.

[0087] For example, when the reliability is above an upper threshold, the control module is allowed to perform active control (charging limit, current bypass, cooling enhancement, etc.), and when the reliability is below a lower threshold, it is designed to perform only conservative control or prioritize the judgment of the reflective emergency control layer even if the same risk level is calculated.

[0088] This upper and lower threshold structure has a technical necessity to prevent excessive blocking during the initial operation phase when AI predictions are incomplete or when sensor abnormalities are suspected, while enabling more aggressive accident prevention when confidence in the prediction is high.

[0089] The AI ​​diagnostic module provides prediction results to the control module to enable stepwise control, such as charging restriction, current bypass, circuit interruption, and cooling enhancement. Furthermore, it is closely coupled with the physical cooling / thermal management structure through linkage with the reflective emergency control layer and the cooling control module. Specifically, when situations directly related to thermal management are detected—such as operating modes where cooling margin is insufficient due to high ambient temperatures, cases where heat is concentrated in specific modules due to significant temperature imbalances between modules, or cases where cooling performance is judged to have deteriorated because the cooling channel sensor outputs abnormal values—the diagnostic module determines the risk level to be higher, even if the risk level is the same, or prioritizes setting an emergency cooling request flag to the cooling control module. Conversely, if it is determined that there is sufficient cooling margin based on the inlet-outlet temperature difference and flow rate information measured by the cooling control module, the risk level is adjusted gradually for the same internal temperature to reduce unnecessary shutdowns. In this way, AI judgment and physical cooling control are combined through a mutual feedback structure, which is an important feature of the present invention that distinguishes it from simple temperature threshold-based cooling methods.

[0090] The reflective emergency control layer can be implemented as a physical circuit consisting of an independent microcontroller (MCU), an analog comparator, a hardware threshold circuit, a MOSFET driving circuit, a TVS, and an RC snubber, and is connected to the data collection module via a path separate from the artificial intelligence diagnostic module.

[0091] The AI ​​diagnostic module receives log data regarding how many times the reflection control layer actually performed tripping and bypassing operations under specific sensor conditions, the temperature and current values ​​of the cell, module, and pack at the time of tripping, the duration of the tripping hold, and the time required for temperature and current to stabilize after tripping. By analyzing this history, the diagnostic module distinguishes between events that led to actual failures and malfunctions caused by simple noise or sensor errors. It also learns recurring false tripping patterns to correct for such patterns, ensuring that the risk level is not excessively inflated during future predictions. Simultaneously, if a persistent discrepancy occurs between its prediction results and the actual operation of the reflection control layer, the diagnostic module incorporates this into the reliability calculation process. In specific operating ranges, it adjusts its strategy to prioritize the judgment of the reflection control layer and assign a lower weight to the AI ​​output. This structure supports the "technical necessity for AI control and the reflection control layer to operate in parallel" and forms a dual safety structure that mutually monitors and corrects to prevent erroneous judgments by either party from leading to fatal consequences.

[0092] In addition, the AI ​​diagnostic module performs self-relearning by periodically accumulating battery status history, actual failure records, service and maintenance history, operation logs of the cooling control module, and communication error history accumulated during operation.

[0093] Retraining can be performed in parallel by separating long-term accumulated data from short-term real-time data. Short-term data is rapidly trained on a lightweight model installed on edge computing devices to improve the immediate prediction accuracy of field terminals, while long-term data undergoes more sophisticated analysis on a cloud or central server to correct basic model parameters. During this process, operating conditions such as ambient temperature, operating mode (e.g., high-power mode, standby mode), cooling pump operation patterns, and communication quality are also learned, allowing threshold values ​​and reliability calculation logic to be automatically adjusted according to the environment. Consequently, even when the same model is deployed to sites with diverse climates and load conditions, a certain level of prediction performance and stability is maintained.

[0094] Furthermore, in this embodiment, a reinforcement learning-based update algorithm may be applied.

[0095] In reinforcement learning, the control module actually performs a specific control (charging limit, bypass, cutoff, cooling reinforcement, etc.) based on the risk level calculated by the AI ​​diagnostic module, and evaluates how quickly the temperature and current returned to a safe range as a result. If the response was appropriate, a reward is given, while if there was an excessive cutoff or a delayed response, a penalty is imposed. Through this reward system, the diagnostic module goes beyond simply simulating past data and autonomously optimizes its policy by incorporating actual control results as feedback. This is particularly advantageous for quickly securing stable diagnostic performance during the initial operation phase when sufficient data has not yet been accumulated, or in environments where new cooling structures or module configurations are applied.

[0096] Furthermore, the AI ​​diagnostic module may include an environment-adaptive correction unit. This unit recognizes ambient temperature, humidity, operating mode, flow rate and inlet / outlet temperature difference of the cooling path, the degree of battery pack aging, and communication quality, and automatically adjusts the threshold values ​​or variable-specific weights used by the prediction model. For example, in high-temperature summer environments where sufficient cooling flow is secured, higher weights are assigned to the rate of temperature increase and the rate of internal resistance increase rather than the absolute temperature value; conversely, in low-temperature winter environments, the sensitivity of the current-based judgment logic can be adjusted by considering voltage drop and increased internal resistance during charging. Additionally, if the cell's internal resistance gradually increases during long-term operation, the diagnostic module learns this and reduces unnecessary alarms by raising the acceptable voltage drop threshold for normal operation. These environment-adaptive correction functions and dynamic threshold adjustment logic serve as elements that specifically implement the "reason why automatic threshold correction is necessary," providing features that clearly distinguish them from conventional technologies that rely on fixed threshold values.

[0097] According to the above configuration, the artificial intelligence diagnostic module according to the present invention detects early signs of failure through time-series learning that considers the hierarchical structure extending from cell to cell group to module to pack, the cooling system, reflection control hardware, and the operating environment, rather than a simple threshold comparison method, and enables the rational determination of the control intensity to be performed by the control module, reflection control layer, and cooling control module by dynamically correcting the threshold value according to prediction reliability and environmental conditions.

[0098] Furthermore, malfunction patterns are automatically corrected through mutual feedback with the reflection control layer, and long-term accuracy can be improved through the introduction of reinforcement learning and hybrid prediction structures. Scalability is also maintained even in large-scale battery pack or multi-system environments through distributed learning between the edge and the cloud. Ultimately, the AI ​​diagnostic module serves as a core intelligent engine that organically connects AI prediction control, reflection control, and cooling control, contributing to the realization of a self-correcting battery safety management structure where the entire system learns and stabilizes on its own.

[0100] In one embodiment of the present invention, the control module determines the risk level for each layer—cell unit, cell group unit, module unit, and pack unit—step by step based on the failure probability, expected failure time, and prediction reliability index provided by the artificial intelligence diagnostic module, and performs different control policies according to the determined risk level and system operating status.

[0101] FIG. 3 is a simplified block diagram showing the control flow and switching path configuration of an automatic cutoff module according to one embodiment of the present invention.

[0102] In this drawing, a control signal from the control module controls the on / off state of the MOSFET switching element through the gate driver, and the MOSFET switching element performs the role of blocking or connecting the current flow between the battery cell and the load. The protection circuit absorbs surge voltage or transient current that may occur during the switching process to prevent damage to the MOSFET, and current is supplied to the load after passing through the entire protection circuit. Through this, the present invention implements a cutoff system that integrates not only AI prediction-based control but also an electrical protection structure.

[0103] As illustrated in the drawing, the control module (300) acts as a higher control layer of the automatic cutoff module and determines key control parameters such as the switching opening / closing time, gate voltage rise rate, current ramp rate, and cutoff / re-closing policy, and transmits them to the gate driver (420). The control module determines the intensity and timing of the switching operation by considering the failure probability, expected failure time, reliability index, and event information of the reflective emergency control layer provided by the artificial intelligence diagnostic module, and also determines whether the protection circuit is saturated and the switching-capable state based on temperature, voltage, and current data collected in real time from the sensor network.

[0104] The gate driver (420) is a circuit for converting a control signal input from a control module into a driving voltage and current form required by a MOSFET switching element, and performs signal isolation, pulse width control, gate charge / discharge current control, and dv / dt control functions. This driver stably manages the on / off switching speed of the MOSFET to minimize transient response that may occur during switching, and performs stepwise or delayed switching as needed to reduce voltage and current stress on the entire system.

[0105] The MOSFET switching element (410) is a core component of the automatic cutoff module and opens and closes the current path between the battery cell and the load in response to control provided through the gate driver. The drain and source terminals of the MOSFET are directly connected to the main current path to handle the conduction and interruption of current, and maintain low on-resistance characteristics during conduction to minimize power loss. During cutoff, a high voltage spike is induced by the sudden change in current, which can be further amplified by the inductance component of the battery module and the switching speed (dv / dt, di / dt), so it is necessary to link with a subsequent protection circuit.

[0106] The protection circuit (430) is a circuit for absorbing and mitigating transient voltage, surge current, ringing, and high-frequency noise generated in the MOSFET switching element, and may include one or more components such as a TVS diode, an RC snubber, and a gate soft start element. The protection circuit operates in stages in conjunction with the switching timing of the MOSFET, and limits the peak voltage at the moment of switching and disperses energy to prevent damage to the switching element and adjacent circuits. In addition, the protection circuit is checked in real time for saturation, degradation status, and residual voltage through coordinated operation with the control module, thereby ensuring safety when re-energized after shutdown.

[0107] Finally, the load (battery cell) is a power consuming or storage element to which the automatic cutoff module supplies or cuts off current, and it is the section involving the entire system output. By regulating the current delivered to the battery cell through switching elements, the automatic cutoff module can immediately cut off the current at the cell-by-cell or cell-group level in the event of a dangerous situation, and can perform a safe recovery procedure in coordination with the protection circuit.

[0108] Accordingly, FIG. 3 shows the overall processing flow in which a switching control signal originating from a control module passes through a gate driver and a MOSFET switching element, then through a protection circuit, and is transmitted to a battery cell. By doing so, it provides a structural basis for explaining that the automatic cutoff module of the present invention performs prediction-based switching, transient response control, and protection circuit-linked control in an integrated manner, going beyond simple switching functions. The stepwise signal flow as illustrated in this embodiment plays a key role in improving the stability, response speed, durability, and recovery capability of a high-power battery system.

[0109] Referring to FIG. 3, an embodiment of the present invention is described as follows: the control module manages upper and lower threshold values ​​set for each layer, as well as upper and lower reliability limits corresponding to the prediction reliability. It determines the control intensity by comprehensively considering "how reliable the predicted risk is" and "how much margin the current cooling state and external environment have," rather than simply looking at whether the value exceeds the threshold.

[0110] For example, even if the same failure probability is calculated, gradual control is applied when the cooling flow rate is sufficient and temperature imbalance between modules is minimal; however, when cooling performance is degraded or temperatures rise abnormally only in specific modules, a more conservative threshold is applied to rapidly switch to strong control. This structure is designed to overcome the limitations of conventional technology, where fixed threshold-based control fails to reflect environmental changes and thermal management conditions, and is based on the technical necessity for the control module to simultaneously monitor both predicted results and the physical thermal management status.

[0111] The control module compares the hierarchical failure probability calculated by the artificial intelligence diagnostic module with upper and lower threshold ranges that are pre-set or dynamically corrected during operation, and simultaneously evaluates the prediction reliability index to adjust the control level and intensity.

[0112] If the reliability index is higher than the upper threshold and the failure probability for a specific cell or cell group exceeds the upper threshold of that layer, the control module determines that there is sufficient grounds to immediately perform robust control on that layer. Consequently, it outputs a full shutdown command at the cell or cell group level to the automatic shutdown module and simultaneously requests the cooling control module to enter emergency cooling mode. Conversely, if the predictive reliability is below the lower threshold or the uncertainty is assessed as high due to degraded input data quality, the control module prioritizes conservative control—such as limiting the charging current to a certain rate or increasing the cooling flow rate for the relevant module—even if the same failure probability is calculated. It then proceeds to enter the shutdown phase only after re-observing the trend for a certain period. If the reliability index falls within the intermediate range between the upper and lower thresholds, the control module performs intermediate-level control, such as priority charging restriction, enhanced local cooling, or partial bypass at the cell group level. It switches to full shutdown only when the failure probability continues to show an increasing trend during a certain monitoring period or when repetitive abnormal events are detected by the reflective emergency control layer.

[0113] This reliability-based multi-stage control policy is intended to reduce excessive blocking that may occur when following AI results with prediction errors as is, while ensuring the speed of response to potential risks, and clearly demonstrates the technical justification for operating by dividing the prediction reliability threshold into upper and lower limits.

[0114] The control module hierarchically controls the switching element section of the automatic cutoff module according to the risk level and control stage determined as above. As shown in FIG. 3, the automatic cutoff module may include a MOSFET switching element, a gate driver section that drives it, and a protection circuit section composed of a transient voltage suppressor (TVS), an RC snubber circuit, and a gate soft start circuit.

[0115] A control signal from the control module is transmitted to the gate driver to control the on / off state of the MOSFET, and the protection circuit absorbs transient voltage and current spikes that may occur at the time of switching to prevent damage to the switching element and adjacent circuits. At this time, the control module does not merely turn the switch on and off, but also controls the gate voltage rise rate, current ramp rate, cutoff hold time, reconduction delay time, and switching sequence together, thereby ensuring that the switching operation is performed within the thermal and electrical allowable range of the protection circuit.

[0116] For example, even when multiple cell groups must be blocked simultaneously, the control module prevents excessive surge current from concentrating at a single point in time by operating with a fine time difference for each group.

[0117] After the switching operation is completed, the control module receives current feedback signals, the on-resistance status of the switching element, and temperature and voltage stress information of the protection circuit from the automatic cutoff module to verify whether the actual cutoff was performed normally.

[0118] When conditions such as overheating, TVS saturation, or excessive heat generation in the snubber circuit are detected in the protection circuit, the control module delays the re-energization of the path for a certain period of time or instructs the cooling control module to intensify local cooling, thereby withholding the return until the hardware stress is sufficiently relieved. This process determines the return by considering not only whether the current and temperature have stabilized but also the recovery state of the protection circuit itself, thereby ensuring the physical validity of the reversible control loop.

[0119] Even after the shutdown based on the anomaly prediction is completed, the control module continuously monitors the operating status of the automatic shutdown module and the possibility of current restoration in the battery cells, cell groups, and modules.

[0120] The control module evaluates the degree of stabilization of sensor data such as temperature, current, voltage, and impedance provided by the data collection module, the current ripple level, the trend of temperature decrease or equilibrium, whether the temperature difference between the inlet and outlet of the cooling channel has decreased, the latest prediction results of the artificial intelligence diagnostic module and their reliability index, and the presence of newly detected abnormalities in the reflective emergency control layer.

[0121] When these information satisfies predefined return conditions, such as maintaining a state below a threshold value for a certain period of time, the time derivatives of current and temperature converging within a certain range, or the temperature of the protection circuit decreasing below a reference, the control module starts a reversible control loop and gradually restores the blocked circuit.

[0122] In the recovery phase, the soft-start circuit of the gate driver is activated to prevent a rapid rise in current, and the entire circuit is reconnected step-by-step by performing sequential bypass release and reconduction at the cell or cell group level. During the recovery process, the cooling control module temporarily increases the cooling flow rate for some modules to help keep residual heat generated during the re-energization process and the temperature rise caused by recharging within a stable range. After recovery, the control module checks whether the failure probability and prediction reliability index for each layer provided by the AI ​​diagnostic module remain stably below the critical range for a certain monitoring period, and continues monitoring until normal recovery is fully verified.

[0123] The control module of this embodiment does not merely passively follow the AI ​​diagnostic results, but performs integrated control that considers prediction reliability, sensor data quality, the state of the cooling system, the response characteristics of the hardware circuit, and the operational history of the reflective control layer. For example, if the reflective emergency control layer performs immediate blocking for a specific cell group, the control module automatically suppresses or delays additional blocking commands or rapid re-insertion for that path to prevent circuit stress from being exacerbated by overlapping commands. Furthermore, if the blocking conditions of the reflective control layer and the prediction results of the AI ​​diagnostic module repeatedly differ, the control module evaluates the possibility of malfunction of the reflective control layer and can reset the threshold, debounce time, and return delay time, or switch to a strategy that prioritizes AI control in that section. Conversely, if the reliability of the AI ​​diagnostic results rapidly deteriorates due to communication errors or data loss, the control module prioritizes the physical threshold determination of the reflective control layer and limits AI-based control to an auxiliary level, thereby ensuring that minimum safety functions are maintained even in the event of a communication failure.

[0124] In addition, the control module records the response times of each cell and cell group, the saturation frequency of the protection circuit, surge suppression performance, and the cooling effect of the cooling control module based on past interruption and recovery history, and feeds this data back to the artificial intelligence diagnostic module and the reflection control layer to progressively correct the critical range and control parameters.

[0125] For example, if a recurring pattern of similar temperature rise is observed in a specific module but does not lead to actual failure, the control module can modify its control strategy to prioritize increasing the cooling flow rate for that module instead of slightly raising the temperature threshold. Conversely, if a group of cells is found to fail rapidly even with small temperature rises, the policy can be strengthened by lowering the threshold for that group and prioritizing the allocation of available cooling resources. This history-based parameter correction enables the control module to adapt to the actual characteristics of the system over time and allows for finer step control even in the same environment.

[0126] Consequently, the control module of the present embodiment integrally comprises (i) a multi-stage logic that subdivides control intensity according to prediction reliability, (ii) precise cutoff and reset control through cooperation with an automatic cutoff module, (iii) a double and triple safety structure through mutual feedback with a cooling control module and a reflection control layer, and (iv) an automatic correction function for threshold values ​​and control parameters using accumulated data during long-term operation. Accordingly, the system of the present invention significantly improves prediction accuracy, response speed, safety, and operational continuity compared to a conventional simple relay cutoff-based BMS, and simultaneously ensures the lifespan and stability of the battery module even in high-power operating environments such as electric vehicles, heavy equipment, and large-capacity energy storage devices where high-speed charging is performed.

[0128] FIG. 4 illustrates a risk-level-based control flow according to one embodiment of the present invention.

[0129] The data collection module records actual sensor data into the state storage, while the AI ​​diagnostic module exchanges predicted values, reliability, and learning history with the state storage. The control module stores risk levels and control policy change information in the state storage to utilize for subsequent analysis and recovery decisions. The automatic shutdown module and the reflective emergency control layer transmit actual switching operation logs and immediate shutdown history / malfunction patterns, respectively, to the state storage, providing foundational data that enables the entire system to learn and correct over the long term. The state storage aggregates this data and feeds it back to the AI ​​diagnostic module and control module, thereby continuously improving the fault prediction accuracy and control stability of the entire battery system.

[0130] As illustrated in FIG. 4, the control module receives the failure prediction results (failure probability P, expected time of failure, prediction reliability index, etc.) input from the artificial intelligence diagnostic module, and first performs a risk level determination logic based on the predicted values.

[0131] In this embodiment, if the failure probability (P) exceeds a preset upper threshold, it is determined to be high risk (RED); if it is below a lower threshold, it is determined to be low risk (GREEN); and if it is located in the area between them, it is determined to be medium risk (YELLOW).

[0132] This classification can be dynamically adjusted by considering not only simple probability comparisons but also reliability indices, the stability of sensor data, and whether immediate danger is detected in the reflective emergency control layer.

[0133] Once the risk level determination is complete, the control module selects a control policy based on the corresponding level. In the case of a RED (high risk) level, a complete shutdown command is immediately generated and transmitted to the automatic shutdown module; for a YELLOW (medium risk) level, conservative controls such as charging limits, current limits, and enhanced cooling are prioritized. If classified as a GREEN (low risk) level, the battery module is maintained in a normal operating state, and no additional intervention is performed. This risk-level-based policy is designed to prevent unnecessary shutdowns while enabling a rapid response to critical failures, such as sudden thermal runaway or short circuits.

[0134] When a control policy by grade is determined, the control module transmits it to the automatic cutoff module (400) to enable actual control to be performed. The automatic cutoff module consists of a MOSFET switching element and a protection circuit, and performs operations such as current interruption, bypassing, and partial interruption according to commands transmitted from the control module. In particular, in high-risk situations, switching operations occur immediately, and the protection circuit (TVS diode, RC snubber, soft start circuit, etc.) suppresses spikes and transient voltages, thereby safely interrupting the current path.

[0135] After the automatic cutoff module performs control, the control module executes the operating status monitoring stage. In this stage, the control module comprehensively evaluates temperature, current ripple, voltage stability, protection circuit saturation status, and signals from the reflective emergency control layer collected through the sensor network. If all conditions reach the normal range and the risk level of the AI ​​prediction results has stably decreased, the control module performs reconnection (reversible control loop) to gradually restore the cutoff circuit. During the reconnection process, the soft-start function is activated to allow the current to increase slowly, and if necessary, sequential reconduction is performed by cell group to prevent current peaks that may occur upon re-energization.

[0136] As such, the risk-level-based control procedure illustrated in FIG. 4 is not a simple threshold-based control, but an integrated decision-making structure in which AI prediction, sensor data, and a hardware immediate response layer are linked, enabling immediate shutdown in high-risk situations, conservative control in medium-risk situations, and stable operation in normal situations. Furthermore, since safe circuit restoration is possible through continuous monitoring even after shutdown, the present invention provides the effect of simultaneously improving the availability and lifespan of the battery system in a high-output environment.

[0137] An embodiment of the present invention will be described with reference to FIG. 4.

[0138] The automatic cutoff module is a hardware layer that selectively cuts off or bypasses current paths at the cell unit, cell group unit, or module unit level according to control commands transmitted from the control module and the reflective emergency control layer, and includes a protection circuit to suppress transient voltage and surge current that may inevitably occur due to switching operations during this process.

[0139] Since the automatic cutoff module is directly connected to the physical layer of the battery pack / module / cell structure and serves as the final safety device that actually cuts off the current to each cell or cell group, it is essential that it be designed with an electronic structure capable of withstanding high-speed charging and discharging and repeated switching, going beyond the level of a simple relay.

[0140] Accordingly, the automatic cutoff module of the present embodiment is composed mainly of electronic relays or semiconductor switching devices such as MOSFETs, IGBTs, GaN-FETs, and SiC-FETs, and a structure that directly cuts off the current using only mechanical relays is not used in order to prevent contact bouncing, increased contact resistance, and reduced durability due to wear that commonly occur in mechanical contacts.

[0141] Specifically, the drain and source terminals of the MOSFET switching element are arranged in series or parallel in the main current path between the battery cell or cell group and the load (or busbar) to directly control the current flow, and the gate terminal receives a digital control signal transmitted from the control module to determine the open / closed state. Since the logic level signal from the control module is difficult to drive the MOSFET stably as is, a separate gate driver is provided inside the automatic cutoff module to amplify, isolate, and shape the control signal, and then apply a gate voltage with a predetermined rise and fall time.

[0142] This gate driver adjusts the driving voltage and driving current according to the rated voltage, rated current, and operating environment of the switching element, and plays a role in minimizing transient response and reducing electromagnetic interference (EMI) by controlling the gate charging and discharging speeds during the switching process. In addition, in systems where multiple cell groups are configured in parallel, it can also perform the function of dispersing high-current steps at the same time by controlling the driving timing of each switching element with a fine time difference.

[0143] When switching operations occur, particularly at the moment when a circuit carrying a large current is instantaneously cut off, it is physically inevitable that high voltage spikes and ringing will occur due to wiring inductance, distributed capacitance, current imbalance between cells, load characteristics, etc. Since such transient voltages can damage the drain-source insulation layer of the switching element or introduce excessive electromagnetic noise into adjacent circuits, thereby causing malfunctions in other control circuits, the automatic cutoff module necessarily includes a protection circuit to suppress this.

[0144] The protection circuit of this embodiment may include one or more of a transient voltage suppressor (TVS) diode, an RC snubber circuit, and a gate soft start circuit, and these may be used in combination as needed. The TVS diode conducts rapidly the moment a set clamping voltage is exceeded, thereby limiting the voltage across the switching element and preventing insulation breakdown of the internal chip and damage caused by transient voltage. The RC snubber circuit is connected in parallel between the drain and source or between the primary and secondary sides of the MOSFET to mitigate the voltage rise rate (dv / dt) and absorb and dissipate high-frequency components generated during switching, thereby reducing ringing and EMI. The gate soft start circuit controls the rise rate of the gate voltage to mitigate the current rise rate (di / dt) during turn-on and suppresses surge current caused by rapid current conduction. These protection devices are designed with rated and constant values ​​considering the system's nominal voltage, number of cells, maximum current, switching frequency, and cooling efficiency, and can adopt not only a simple configuration using only one protection device but also an active configuration in which multiple protection devices are operated in stages.

[0145] The control module does not treat the protection circuit of the automatic cutoff module as a simple passive element, but operates it as an active protection structure linked to its own control policy. When a cutoff command occurs, the control module first transmits a signal to the automatic cutoff module to enter the "protection ready" state, activating the gate driver and protection circuit, and adjusts the switching timing and current ramp rate so that the TVS diode and RC snubber can absorb sufficient energy.

[0146] Specifically, the control module determines the tripping speed by considering the expected time of failure, prediction reliability, cooling status, and events of the reflective emergency control layer, and can select rapid tripping in situations where the risk of failure is high and cooling margin is insufficient, and gradual tripping in environments where the risk is not high but the environment is sensitive to surges.

[0147] During the cutoff phase, the TVS diode intensively absorbs energy in the peak voltage region, and the RC snubber circuit dissipates the remaining energy in the low-frequency region. Subsequently, during the reconduction phase, the gate soft-start circuit controls the current rise rate, ensuring that the entire switching event terminates within the design tolerance of the protection circuit. In this way, the driving sequence and timing of the protection circuit can be actively adjusted by the control module based on a predefined sequence or sensor feedback.

[0148] The control module also periodically monitors the saturation status of the TVS diode, the charge state of the RC snubber, the temperature rise of the switching element, and changes in on-resistance from the automatic cutoff module.

[0149] For example, if multiple high-power cutoff events occur in succession and there is a risk that the accumulated absorbed energy of the TVS diode will exceed the allowable limit, the control module delays the re-energization of the path for a certain period of time and instructs the cooling control module to strengthen local cooling. In addition, by adjusting the timing of the gate soft start circuit so that the gate is opened only after the residual voltage of the protection circuit has sufficiently dissipated when switching from cutoff to reconduct, the switching is prevented from being repeated before the protection circuit is fully recovered.

[0150] A structure in which the control module recognizes the status of the protection circuit and actively adjusts the timing in this manner incorporates the protection circuit as part of the control loop rather than as a simple surge absorber, thereby significantly improving durability and stability during repeated operation.

[0151] The protection circuit of the automatic cutoff module may have different ratings and response characteristics for different layers, such as cell units, cell group units, and module units, and the control module manages the protection circuit status for each layer separately.

[0152] For example, cell-level switching devices handle relatively small currents but have a high switching frequency, while module-level switching devices handle large currents but may have a low switching frequency.

[0153] Reflecting these characteristics, the control module sets parameters to prioritize fast response and high repeatability for cell-level protection circuits, and high energy absorption capability and sufficient cooling margin for module-level protection circuits. Additionally, when using IGBTs, GaN-FETs, and SiC-FETs in high-voltage environments, the switching speed and dv / dt tolerance of each device differ; therefore, the control module automatically readjusts the gate drive voltage, dv / dt·di / dt limits, and protection circuit timing according to the device type. In particular, since GaN or SiC devices have very high switching speeds that can cause significant surges and ringing, the control module prevents device damage by correcting the constant values ​​of the RC snubber and soft-start circuits during operation based on temperature rise and voltage stress data.

[0154] The control module and the automatic cutoff module maintain the durability and accuracy of the protection circuit through mutual feedback during long-term operation. Whenever the protection circuit absorbs an overvoltage, the control module records the estimated absorbed energy amount and peak voltage level. If the accumulated energy approaches the designed life limit or if a tendency for the clamping voltage of the protection device to gradually rise is detected, it can output a warning signal or generate an alarm recommending maintenance or replacement. This durability management function enables the early detection of a state where "protection appears to have operated but the actual protection capability is degraded" due to the long-term degradation of TVS diodes or switching devices, thereby enhancing the long-term reliability of the entire system.

[0155] Ultimately, in the embodiment according to claim 4, the automatic cutoff module improves stability and durability by limiting all transient responses occurring during cutoff and reconduction to a controllable range through protection circuit hardware closely linked to the stepwise control logic of the control module, and by providing switching and protection strategies suitable for each layer of the cell, cell group, and module.

[0156] This configuration goes beyond simply suppressing surges and functions as an intelligent circuit protection mechanism that automatically corrects protection parameters by reflecting feedback from the artificial intelligence diagnostic module, the reflective emergency control layer, and the cooling control module.

[0157] Therefore, the system can be safely maintained even under operating conditions involving repeated rapid charging and discharging, current peaks, and significant changes in the external environment in high-output lithium-ion battery modules, and it provides the effect of significantly improving the reliability and lifespan of battery systems in various application fields such as electric vehicles, heavy equipment, and energy storage systems.

[0159] A reflective emergency control layer (500) according to one embodiment of the present invention is located between the data collection module (100) and the automatic cutoff module (400) as shown in FIG. 1, and constitutes a hardware-based emergency protection layer that operates prior to the software prediction judgment procedure of the artificial intelligence diagnosis module (200).

[0160] Since the reflective emergency control layer of this embodiment is implemented as an independent path physically separated from the control module (300), it can realize an immediate blocking response in microseconds (ìs) without being affected by problems of the upper control layer such as communication delay, processor error, or bus collision.

[0161] First, the reflective emergency control layer directly receives voltage, current, temperature, and impedance data from the data acquisition module (100), as well as voltage fluctuations across the switching element associated with the circuit structure of FIG. 3 and response signals from the protection circuit unit (430). The signals are evaluated through an array of ultra-high-speed comparators within the reflective control layer, and an analog threshold generator is connected to each comparator. In the temperature comparison channel, a trigger signal is immediately generated if the rise rate (dT / dt) of the thermal sensor exceeds a reference rise rate; in the current comparison channel, a spike that rises by more than a specific multiple relative to the average current is detected; and in the voltage comparison channel, a cutoff signal is immediately output if an inter-cell imbalance (ÄV) or a sudden drop is observed. This analog-based first-order comparison is configured to respond immediately at the hardware level without computational delay.

[0162] In addition, the reflective emergency control layer of the present embodiment is not limited to a single threshold comparison and includes a multipath blocking structure and a time delay-based dual judgment mechanism.

[0163] The first decision path consists of a full hardware comparator that generates a blocking signal in s units, and the second decision path includes a low-power digital signal processing unit (small MCU or DSP core) that re-evaluates the signal after a short delay of 2 to 3 ms. If an anomaly is detected in both paths, definitive blocking is performed to prevent unnecessary total blocking due to a single channel malfunction, while providing a highly reliable double-confirmation structure in actual thermal runaway or short-circuit situations. It is designed so that the reflection control layer can operate preemptively prior to the RED risk level judgment shown in Fig. 4.

[0164] On the one hand, the reflective emergency control layer includes a self-calibration memory. This memory stores raw data for a certain period regarding the temperature rise slope (dT / dt), current spike width and duration, voltage fluctuation rate, saturation status of the protection circuit (TVS·RC snubber), and the response time of the MOSFET gate voltage at the time the reflective control event occurred. Subsequently, during the synchronization process with the artificial intelligence diagnostic module (200) of the present invention, if the AI ​​determines whether the event is an actual failure or a false alarm (false positive), the reflective control layer automatically corrects the threshold value based on this.

[0165] For example, if it is determined that there is no fault under the same conditions, the threshold of the corresponding sensor is slightly raised to reduce sensitivity, and conversely, if it is confirmed to be actual thermal runaway, the threshold is lowered to increase sensitivity. As a result, the reflection control layer operates not as a simple hardware interrupter, but as a reactive safety circuit that learns and corrects with AI.

[0166] In addition, the reflection control layer of the present embodiment has a multi-stage hardware response structure that goes beyond a simple blocking signal generation function.

[0167] A multi-step reaction can be configured as follows.

[0168] Step 1: Form a PWM-based current damping loop to instantaneously reduce the current but not immediately cut off the circuit.

[0169] Step 2: Turn off the MOSFET switching element (410 in FIG. 3) to completely cut off the main current path.

[0170] Step 3: By connecting bypass paths in parallel to distribute residual current to a safe path. In particular, the bypass path ensures the continuity of operation for the remaining cells without interrupting the entire system when a sudden temperature rise is detected in a specific cell. This structure is highly advantageous in high-power environments such as heavy equipment, electric vehicles, and ESS.

[0171] The reflection control layer can transmit a signal to the control module (300) to output a command to increase the cooling flow rate in parallel according to the sensor rise pattern, or to selectively block only a specific module.

[0172] In addition, the reflection control layer of this embodiment includes a cross-validation function between sensors and is designed to prevent malfunction caused by single sensor noise.

[0173] If there is no temperature rise even if a current spike is present, a re-verification routine is executed; if there is a temperature rise but the current does not fluctuate, it is determined to be due to environmental disturbance or a temporary error, and the judgment is re-executed after a certain delay. To this end, a low-power MCU can be installed in the reflection control layer to enable simple digital logic and pattern analysis.

[0174] When the reflection control layer outputs a blocking signal, the signal is directly transmitted to the gate driver (420) of the automatic blocking module (400) shown in FIG. 3 to perform an immediate off operation of the MOSFET switching element (410). The protection circuit (430) (TVS diode, RC snubber, gate soft start circuit) absorbs the transient voltage generated by the sudden blocking and simultaneously transmits the corresponding history to the control module (300) and the artificial intelligence diagnostic module (200) to be utilized in subsequent learning and threshold correction relationships.

[0175] As a variation, the reflection control layer may be composed of an on-device local reflection control layer at the cell or module level, in addition to an integrated structure.

[0176] In this case, a micro comparator, bypass circuit, and micro-pattern memory are inserted inside each cell module to perform local blocking, and the upper control module aggregates this to determine whether to block the entire system. Furthermore, the reflection control layer can be combined with unconventional sensors, such as gas sensors, expansion sensors, and optical-based anomaly detection, to detect early precursors prior to physical runaway, and can also be combined with passive protection elements, such as PTC thermistors, which automatically increase resistance during current transients.

[0177] With the above configuration, the reflective emergency control layer of the present invention realizes immediate hardware response, multi-stage blocking and bypass-based safety assurance, AI-linked threshold correction, distributed local protection, and a hardware-software integrated feedback loop at a stage prior to AI-based predictive control, thereby significantly improving the safety, prediction accuracy, self-correcting capability, and operational continuity of the entire system even in a long-term operating environment.

[0179] An artificial intelligence diagnostic module according to one embodiment of the present invention periodically collects and stores the operation history of a reflective emergency control layer, and performs the function of learning and correcting malfunction patterns by analyzing in detail blocking events that occurred in a reflective control circuit based on the stored history.

[0180] The reflexive emergency control layer performs immediate blocking or bypass operations based on hardware comparators or ultra-high-speed decision circuits without the intervention of artificial intelligence judgment; while this structure is highly advantageous in terms of response speed, it has limitations in that it can react sensitively to transient anomalies caused by thermal noise, sensor drift, wiring resistance fluctuations, electromagnetic interference, etc.

[0181] To complement these hardware characteristics, the AI ​​diagnostic module stores multiple parameters such as temperature, voltage, current, impedance, sensor-to-sensor ratio, rise rate, and fluctuation range for all blocking events generated in the reflection control layer, and determines whether a false positive has occurred by comparing this with whether an actual fault has occurred.

[0182] For example, if the reflection control layer detects a momentary current spike and performs a cutoff, but the AI ​​diagnostic module determines that it is not a fault signal based on a comprehensive evaluation of the temperature rise rate at that time, the voltage balance between cells, the charging status, and the history of the preceding minute, the AI ​​diagnostic module records this as a false cutoff event. Conversely, if a rapid temperature rise actually persists or a rapid voltage drop follows the cutoff by the reflection control layer, the cutoff event is classified as a true detection.

[0183] By repeating this classification process over multiple operating cycles, the artificial intelligence diagnostic module can clearly distinguish between abnormal patterns that repeatedly appear in a specific sensor and actual failure patterns, and creates a sensitivity correction map of the reflection control layer based on the accumulated data.

[0184] The generated calibration map is used to adjust at least one of the sensitivity threshold, debounce time, return delay, and multi-sensor cross-check condition used by the reflection control layer. The sensitivity threshold is a reference value for the reflection control layer to detect anomalies and may include a multiple reference for current spikes, a temperature rise rate reference, an allowable range for voltage imbalance, etc.

[0185] The AI ​​diagnostic module corrects by slightly raising the threshold value when false blocks occur repeatedly under the same conditions, and conversely, lowering the threshold value when the true detection rate is high to shorten the response speed.

[0186] The debouncing time is a time filter used to determine an anomaly as valid only when the signal persists for a certain period; it is extended in environments with high sensor noise and shortened in stable environments to induce a faster response. The recovery delay is the minimum waiting time from blocking to circuit restoration; it is adjusted to ensure safety by extending it in sections with a high risk of thermal runaway, and to ensure availability by shortening it in sections where unnecessary blocking was repeated.

[0187] The control module reflects the correction results transmitted from the artificial intelligence diagnostic module into the reflection control circuit in real time and sets them as parameters adjustable during operation. At this time, the control module applies the correction results within a limited range, taking into account the interdependencies between each parameter.

[0188] In other words, upper and lower boundary ranges are pre-set to prevent the response characteristics of the reflection control layer from fluctuating abruptly due to excessive changes in the sensitivity threshold. If the correction value exceeds these ranges, a rollback logic is operated to suspend the application of correction or revert to the previous state. Additionally, when adjusting multi-sensor cross-checking conditions, the control module applies a cross-checking filter in parallel to prevent the inaccurate application of correlations between different physical quantities, such as temperature, current, and voltage.

[0189] The AI ​​diagnostic module may also perform a reinforcement learning-based correction algorithm using correction data from the reflection control layer. The reinforcement learning algorithm evaluates the cutoff timing of the reflection control layer, the subsequent temperature stabilization speed, and the voltage recovery pattern to determine the appropriate correction direction.

[0190] For example, if the temperature quickly returns to the normal range after cutoff, the cutoff is deemed valid, and the sensitivity is maintained or enhanced; however, if the temperature rises after cutoff or the recovery speed is slow, it is determined that the cutoff timing was delayed, and the sensitivity is adjusted upward. This reinforcement learning-based correction is more advantageous for responding to complex environmental conditions than simple statistics-based correction, and it can ensure stable response characteristics over the long term even if changes in the operating environment or sensor degradation occur.

[0191] In addition, the system can adopt a distributed learning structure that synchronizes correction data from the reflection control layer with a central server or the cloud to apply the same correction values ​​to other battery modules of the same model. Through this, multiple battery systems operating in the same environment can share reflection control data to optimize common threshold values ​​and build correction profiles specialized for specific environments (e.g., high-temperature regions, high-output operation, repeated charging cycles, etc.).

[0192] Ultimately, the configuration according to claim 6 of the present invention combines the immediate response capability of the reflection control layer with the learning and correction capability of the artificial intelligence diagnostic module, thereby providing the effect of significantly improving reliability in long-term operating environments while simultaneously securing response speed and judgment accuracy. In particular, it automatically prevents unnecessary blocking due to overreaction, excessive blocking due to errors in setting reference values, or delayed recovery due to improper debounce time settings, and can maintain a balance of safety and availability by adapting in real time to changes in operating conditions or degradation of sensor performance.

[0193] This self-adaptive reflection control structure has significantly higher intelligent judgment capabilities compared to existing single threshold-based protection circuits and has technical significance in advancing the protection technology of high-power lithium-ion battery systems to the next level.

[0194] The description of the presented embodiments is provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present invention. Thus, the present invention is not limited to the embodiments presented herein. The present invention should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein. Explanation of the symbols

[0195] 1000: AI-based fault prediction and response system for lithium-ion battery modules 100: Data collection module 200: Artificial Intelligence Diagnostic Module 300: Control Module 400: Automatic shut-off module 410: MOSFET switching device 420: Gate drive unit 430: Protection circuit section 431: TVS diode 432: RC Snubber Circuit 433: Gate soft start circuit 500: Reflex Safety Layer 600: State storage unit 800: Battery Fat / Module / Cell Structure 900: Cooling control module

Claims

Claim 1 A fault prediction and response system for a lithium-ion battery module comprises: a data collection module that measures battery status data, including temperature, voltage, current, and impedance, at a predetermined interval and provides it to a central processing unit; an artificial intelligence diagnostic module that analyzes the status data provided by the data collection module to learn abnormal signs over time and calculates the probability of failure and the expected time of failure; and a control module that determines a risk level in stages using the prediction results of the artificial intelligence diagnostic module as input and generates commands for charging restriction, current bypass, or circuit interruption according to the level. The system includes an automatic shut-off module that disconnects the corresponding battery cell from the circuit or forms a bypass path in response to a command from the control module, wherein the control module is configured to perform safety control in the order of a limited charging stage, a partial bypass stage, and a complete shut-off stage by controlling the automatic shut-off module in stages when the predicted probability of failure satisfies a threshold range that is preset or corrected during operation, and wherein the artificial intelligence diagnostic module is configured to use temperature, voltage, current, impedance, cell balancing voltage difference, charge / discharge history, and internal resistance fluctuation as input features, and to predict signs of thermal runaway, short circuit, or capacity degradation early using a time-series predictive deep learning model, and to self-relearn model parameters using status data accumulated during operation and actual failure history, and to automatically correct the calculation criteria for prediction accuracy and reliability according to environmental and aging conditions, and wherein the control module receives the predicted failure time and the prediction reliability index together from the artificial intelligence diagnostic module, and prioritizes a complete shut-off command if the prediction reliability index is above an upper threshold, and a charging limit or cooling control if it is below a lower threshold. AI-based fault prediction and response system for a lithium-ion battery module configured to perform a reversible control loop that monitors the operating status of the above-mentioned automatic shut-off module and reconnects the cell to the circuit when the abnormal state is resolved or the prediction result returns to the normal range. Claim 2 delete Claim 3 delete Claim 4 An AI-based fault prediction and response system for a lithium-ion battery module according to claim 1, wherein the automatic cutoff module includes an electronic relay or MOSFET switching element, and further includes a transient voltage suppressor (TVS), an RC snubber circuit, or a gate soft start circuit for absorbing transient voltages occurring during switch switching, configured to suppress surge current and voltage spikes during cutoff. Claim 5 In claim 4, the AI-based fault prediction and response system for the lithium-ion battery module further comprises a reflex safety layer in which, prior to the prediction judgment procedure of the artificial intelligence diagnostic module, if a sudden temperature rise, a sudden change in current, or an abnormal voltage is detected within a predetermined judgment time window, a physically independent hardware reflex control circuit immediately cuts off or diverts the current without waiting for the artificial intelligence judgment, thereby configuring the AI-based predictive control and physical reflex control to operate in parallel to minimize the occurrence of accidents caused by prediction delay. Claim 6 delete

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