Internal short circuit emergency processing system based on solid-state battery

Abstract

The invention relates to the technical field of solid-state battery safety protection, and discloses an internal short circuit emergency processing system based on a solid-state battery. The system comprises a state monitoring module, a short circuit feature extraction module, a risk assessment module and an emergency strategy generation module. The state monitoring module acquires voltage fluctuation data, temperature gradient distribution data and interface impedance change data of the solid-state battery in real time; the short-circuit feature extraction module analyzes and identifies a voltage sag rate, a thermal runaway propagation gradient and an ion migration abnormal coefficient through a time domain and a frequency domain; the risk assessment module calculates a dynamic risk level; and the emergency strategy generation module generates a local current limiting, regional isolation or global power-off strategy according to the risk level. The system realizes early recognition and graded response of the internal short circuit of the solid-state battery, and improves the safety and reliability of the battery system.

Description

Technical Field

[0001] This invention relates to the field of solid-state battery safety protection technology, specifically to an emergency handling system for internal short circuits in solid-state batteries. Background Technology

[0002] Solid-state batteries are considered a key development direction for next-generation energy storage technology due to their high energy density and good safety. However, solid-state batteries still face the risk of internal short circuits in practical applications. These short circuits can originate from multiple factors such as lithium dendrite growth, interface degradation, manufacturing defects, or mechanical damage. Unlike traditional liquid electrolyte batteries, internal short circuits in solid-state batteries develop more covertly and propagate faster, and the consequences are more severe once thermal runaway occurs.

[0003] In existing technologies, monitoring of internal short circuits in batteries largely relies on threshold judgments of voltage and temperature parameters, such as setting upper limits for voltage or temperature to issue warnings or disconnect the circuit. These methods have significant shortcomings when dealing with the complex and ever-changing internal states of solid-state batteries: the response of single parameters like voltage and temperature is lagging, making it difficult to capture the subtle characteristics of the initial short circuit; key parameters such as changes in interface impedance and ion migration behavior within solid-state batteries are not effectively incorporated into the monitoring system, resulting in early faults going undetected; and thirdly, existing emergency strategies are mostly "one-size-fits-all" power-off handling, lacking a tiered response mechanism for different stages of short circuit development, which may cause unnecessary system downtime or insufficient response.

[0004] Furthermore, some studies have attempted to use model prediction or artificial intelligence methods for battery fault diagnosis. However, these methods typically rely on large amounts of historical data for training, have limited generalization ability, and are computationally complex, making them difficult to implement in real-time in embedded systems. Internal short circuits in solid-state batteries are sudden and variable, requiring processing systems to possess high real-time performance, high accuracy, and high reliability. Existing technologies have not yet effectively addressed these issues. Therefore, there is an urgent need to develop a system capable of real-time monitoring of multi-dimensional operating parameters, accurate identification of short-circuit characteristics, dynamic assessment of risk levels, and generation of corresponding emergency strategies to address the safety challenges posed by internal short circuits in solid-state batteries. Summary of the Invention

[0005] The purpose of this invention is to provide an emergency handling system for internal short circuits in solid-state batteries to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an emergency handling system for internal short circuits in solid-state batteries, the system comprising:

[0007] The status monitoring module is used to collect multi-dimensional operating parameters of the solid-state battery in real time, including voltage fluctuation data, temperature gradient distribution data, and interface impedance change data.

[0008] The short-circuit feature extraction module is used to perform time-domain and frequency-domain feature analysis on the multi-dimensional operating parameters to identify the core feature indicators of internal short circuits in solid-state batteries. The core feature indicators include voltage descent rate, thermal runaway propagation gradient, and ion migration anomaly coefficient.

[0009] The risk assessment module is used to calculate the dynamic risk level of internal short circuit in solid-state battery based on the core characteristic indicators. The dynamic risk level includes critical risk state, diffusion risk state and failure risk state.

[0010] An emergency strategy generation module is used to generate multi-level emergency response strategies based on the dynamic risk level. The multi-level emergency response strategies include local current limiting control strategies, regional isolation control strategies, and global power outage control strategies.

[0011] Preferably, the short-circuit feature extraction module is further used for:

[0012] Establish a mapping relationship between the aforementioned core characteristic indicators and the degree of degradation of solid-state battery materials;

[0013] The location coordinates and radius of influence of the internal short circuit in the solid-state battery are determined based on the mapping relationship.

[0014] The location coordinates and the radius of influence are transmitted to the risk assessment module.

[0015] Preferably, the risk assessment module is further used for:

[0016] Calculate the thermal runaway propagation rate based on the coordinates of the location where the runaway occurred and the radius of the affected area.

[0017] A three-dimensional risk evolution model is constructed by combining the thermal runaway propagation rate and the core characteristic indicators;

[0018] The three-dimensional risk evolution model is used to predict the development trajectory of internal short circuits in solid-state batteries.

[0019] Preferably, the emergency strategy generation module is further used for:

[0020] The emergency response time window is determined based on the aforementioned three-dimensional risk evolution model;

[0021] Based on the emergency response time window, the execution sequence of the multi-level emergency response strategy is optimized to generate an emergency control instruction sequence containing the execution sequence.

[0022] Preferably, the system further includes:

[0023] An actuator control module is used to parse the emergency control command sequence and drive multiple types of actuators, including a variable impedance regulator, a phase change material injection device, and an electromagnetic shielding array.

[0024] The feedback adjustment module is used to monitor the execution effect of the various types of actuators in real time and generate dynamic adjustment parameters.

[0025] Preferably, the actuator control module is further configured to:

[0026] The deployment location of the variable impedance regulator is determined based on the coordinates of the occurrence location and the radius of the influence range.

[0027] Calculate the injection flow rate and injection pressure of the phase change material injection device based on the dynamic risk level;

[0028] The shielding strength gradient of the electromagnetic shielding array is adjusted according to the thermal runaway propagation rate.

[0029] Preferably, the feedback adjustment module is further used for:

[0030] Real-time operating parameters of the various types of actuators are collected, including impedance adjustment accuracy, material coverage, and shielding efficiency.

[0031] The deviation between the real-time operating parameters and the expected control target is compared, and a parameter correction instruction including the deviation is generated.

[0032] Preferably, the system further includes:

[0033] The strategy optimization module is used to reconstruct the multi-level emergency response strategy according to the parameter correction instructions and the dynamic adjustment parameters;

[0034] The strategy optimization module is also used to establish an emergency response effectiveness evaluation index system, which includes risk suppression efficiency and energy loss coefficient.

[0035] Preferably, the strategy optimization module is further used for:

[0036] The weighting coefficients are optimized based on the risk suppression efficiency and energy loss coefficient.

[0037] The parameter thresholds of the three-dimensional risk evolution model are adjusted based on the optimized weight coefficients of the strategy.

[0038] Update the accuracy of the control parameters in the emergency control command sequence.

[0039] Preferably, the system further includes:

[0040] The data storage module is used to archive historical data of the multi-dimensional operating parameters, core characteristic indicators, dynamic risk levels, and multi-level emergency response strategies.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] This invention improves the accuracy and early warning capability of internal short circuit identification in solid-state batteries by real-time acquisition and fusion analysis of multi-dimensional operating parameters. The system comprehensively utilizes data on voltage fluctuations, temperature gradient distribution, and interface impedance changes, overcoming the limitations of traditional methods that rely on a single parameter. This allows for a more comprehensive reflection of changes in the battery's internal state, avoiding false alarms or missed alarms.

[0043] The short-circuit feature extraction module identifies core feature indicators such as voltage descent rate, thermal runaway propagation gradient, and ion migration anomaly coefficient through joint analysis in the time and frequency domains. These indicators can effectively characterize different stages and evolution trends of short circuit occurrence, providing reliable input for risk assessment.

[0044] The risk assessment module dynamically calculates the risk level based on core characteristic indicators, distinguishing between three states: critical risk, diffusion risk, and failure risk. This achieves full coverage from potential failure to severe failure, enabling the system to adapt to the security requirements of different application scenarios.

[0045] The emergency response strategy generation module activates corresponding levels of response strategies based on the dynamic risk level, including local current limiting, area isolation, and global power outage measures. This multi-level response mechanism avoids over-processing or under-response issues, maintaining system operation as much as possible and extending equipment uptime while ensuring safety.

[0046] The entire system is highly integrated and real-time, suitable for embedded platform deployment, and can make rapid decisions without relying on large amounts of historical data, reducing dependence on computing resources. This system can be widely used in electric vehicles, energy storage power stations, portable electronic devices, and other fields, enhancing the active safety protection capabilities of solid-state battery systems, extending battery life, reducing maintenance costs, and promoting the application of solid-state battery technology in scenarios with high safety requirements. Attached Figure Description

[0047] Figure 1 This is a timing diagram of the solid-state battery internal short-circuit emergency handling system described in this invention.

[0048] Figure 2 A flowchart enhancing the risk assessment module;

[0049] Figure 3 This is a diagram showing the operational results of the solid-state battery emergency response system. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 This invention provides an emergency handling system for internal short circuits in solid-state batteries, the system comprising:

[0052] The condition monitoring module collects multi-dimensional operating parameters of the solid-state battery in real time through a multi-source sensor network. Voltage fluctuation data is acquired using a high-precision voltage sensor at a sampling frequency of 1000 times per second. Temperature gradient distribution data is monitored by a distributed thermocouple array to monitor temperature changes at different locations on the battery surface and inside. Interface impedance change data is measured in real time using AC impedance spectroscopy to measure the impedance characteristics of the electrode-electrolyte interface. The short-circuit feature extraction module performs time-domain and frequency-domain feature analysis on the collected multi-dimensional operating parameters. Time-domain analysis uses wavelet transform to extract abrupt changes in the voltage signal, while frequency-domain analysis uses fast Fourier transform to identify abnormal resonant frequencies in the impedance spectrum, thereby accurately identifying three core characteristic indicators: voltage descent rate, thermal runaway propagation gradient, and ion migration anomaly coefficient. The risk assessment module calculates the dynamic risk level based on the core characteristic indicators. A voltage descent rate exceeding 50 mV / s is marked as a critical risk state; a thermal runaway propagation gradient greater than 10℃ / mm and an ion migration anomaly coefficient less than 0.7 are judged as a diffusion risk state; when all three indicators exceed the threshold simultaneously, it is classified as a failure risk state. The emergency strategy generation module generates multi-level emergency response strategies based on dynamic risk levels. In critical risk states, a local current limiting control strategy is triggered to achieve initial suppression by reducing local current output. In diffusion risk states, a regional isolation control strategy is initiated to block the heat propagation path using physical isolation circuits. In failure risk states, a global power-off control strategy is executed to cut off the energy supply of the entire battery system.

[0053] Example 1: See Figure 2The module receives multi-dimensional operational parameter data streams from the condition monitoring module, including raw voltage fluctuation signals, temperature distribution matrices, and impedance spectrum information. It employs a parallel processing architecture to synchronously analyze the multi-source data. Voltage fluctuation data is processed using time-domain differentiation to extract rate-of-change features; temperature data is processed using a spatial gradient algorithm to calculate thermal conductivity; and impedance data is processed using frequency-domain decomposition to identify phase shifts at characteristic frequency points. A machine learning approach is used to establish the mapping relationship between core characteristic indicators and the degree of solid-state battery material degradation. The module's built-in neural network model uses voltage descent rate, thermal runaway propagation gradient, and ion migration anomaly coefficient as input features. After three hidden layers, it calculates and outputs quantified values ​​of separator thickness decay rate and electrode active material loss rate. This mapping model is trained using historical fault data and can infer the material state based on real-time operational characteristics.

[0054] The location coordinates of the internal short circuit in the solid-state battery are determined using multi-sensor fusion localization technology. Voltage sensors placed at different locations on the battery detect the signal propagation time difference, and the three-dimensional spatial coordinates of the anomaly point are calculated using a time-of-arrival difference algorithm. Simultaneously, data from a temperature sensor array is used, and a heat source localization algorithm is employed to verify the accuracy of the coordinates. The final output includes location information in the xyz coordinate system. Calculating the radius of influence requires comprehensive electrothermal coupling analysis. Using the location coordinates as the center, the heat diffusion process is simulated using the heat conduction equation. Combined with the specific heat capacity and thermal conductivity parameters of the battery material, the thermal influence radius at different time points is calculated. Furthermore, the propagation law of electrical characteristics is considered, and the boundary of the influence range is corrected based on impedance change data.

[0055] The location coordinates and radius of the affected area are transmitted to the risk assessment module using a high-speed data bus protocol. The data packet contains coordinate data, radius value, timestamp, and data checksum, with a transmission rate of 1Gbps to ensure real-time performance. Upon receiving the data, the risk assessment module immediately initiates calculations of the thermal runaway propagation rate. The calculation employs a finite element thermal simulation model, discretizing the battery structure into mesh elements and simulating the diffusion rate of heat in three-dimensional space based on the material's thermal conductivity and the intensity of the heat source. The simulation process considers the interfacial thermal resistance of different material layers and the influence of cooling conditions on the propagation rate, outputting the temperature rise per second and the expansion rate of the thermally affected area.

[0056] A three-dimensional risk evolution model is constructed by combining thermal runaway propagation rate and core characteristic indicators. This model establishes a dynamic probability field with spatial coordinates, time dimension, and risk intensity as axes, integrating thermal propagation rate as a time function and characteristic indicators as spatial distribution functions for calculation. The model simulates the dynamic changes of the risk field by solving a system of partial differential equations. The risk value at each grid point is jointly determined by the local temperature gradient, voltage change rate, and ion mobility. The development trajectory of internal short circuits in solid-state batteries is predicted using a probabilistic prediction method. Monte Carlo simulations are run to simulate multiple possible development paths, with different boundary condition combinations randomly generated for each simulation. The output results include the main directional trajectory of risk diffusion in the future time period, the functional areas that may be affected, and the probability of occurrence of different risk levels. This predicted data provides a basis for emergency strategy generation.

[0057] From feature extraction to risk prediction, a complete analysis chain is established. The short-circuit feature extraction module continuously updates the mapping database to improve positioning accuracy, while the risk assessment module continuously optimizes the parameter settings of the 3D model. Data interaction between the two modules adopts a millisecond-level synchronization mechanism to ensure the real-time performance and accuracy of risk prediction, providing the system with early warning capabilities that anticipate fault development. A multi-level caching design is employed during module processing: raw data is first stored in a high-speed cache for preprocessing, feature extraction results are stored in an intermediate cache for model access, and finally, predicted data is output to shared memory for downstream modules. This architecture ensures the smoothness of the data processing flow and avoids data blocking or loss. Detailed logs are maintained for all calculation processes, including feature extraction parameters, intermediate values ​​from model calculations, and prediction results. These logs provide important references for system performance optimization and subsequent upgrades. Through continuous operation and learning, the system gradually improves the accuracy of identifying internal short-circuit features in solid-state batteries and the accuracy of risk prediction.

[0058] Example 2: The system receives output data from the three-dimensional risk evolution model of the risk assessment module. This data includes the probability distribution of the short-circuit development trajectory inside the solid-state battery and the threshold values ​​of key parameters. The time window calculation unit inside the module immediately initiates emergency response time window analysis. By comparing the risk diffusion curve with the system's preset safe operating boundary, it identifies the operable time interval from the current moment to the point of risk irreversibility. This analysis comprehensively considers the physical inertia of thermal runaway propagation, the mechanical response delay of the actuator, and the inherent delay of control signal transmission, and finally calculates the optimal intervention period corresponding to each risk level. Optimizing the execution sequence of multi-level emergency response strategies based on the emergency response time window requires calculation using a dynamic programming algorithm. The algorithm takes maximizing the risk control effect and minimizing energy loss as dual objective functions, and uses local current limiting control strategy, regional isolation control strategy, and global power-off control strategy as selectable decision variables. The algorithm traverses all possible strategy combinations and time sequences, evaluates the superposition effect of each sequence on the time axis, and finally outputs the start time, duration, and switching conditions of each strategy, forming a tightly connected protection chain in the time dimension.

[0059] Generating an emergency control command sequence with execution timing involves command encoding and protocol encapsulation. Local current limiting control strategies are encoded into timestamped current regulation commands, which include target current values, current drop slopes, and duration parameters. These parameters are dynamically generated based on the risk level and the current battery state. Area isolation control strategies are converted into trigger commands for physical isolation devices, specifying the isolator number to be activated, the trigger delay time, and the isolation duration to ensure that the circuit connection in the designated area is cut off at a precise moment. Global power-off control strategies generate a multi-level power-off command sequence, which includes voltage graded drop curve parameters, a final power-off state confirmation mechanism, and system self-locking time settings.

[0060] All instructions are arranged into an ordered instruction stream according to the calculated execution sequence. Each instruction carries a high-precision time synchronization mark to ensure coordinated operation between different actuators. The instruction sequence is transmitted to the actuator control module through a secure communication protocol. The transmission process employs redundancy check and retransmission mechanisms to ensure the integrity and reliability of the instructions. The emergency strategy generation module continuously obtains execution effect data from the feedback adjustment module to optimize the time window calculation algorithm and strategy timing arrangement. The module has a built-in strategy simulation unit that can simulate the execution effect before the actual issuance of instructions, further adjust instruction parameters and timing arrangements, and form a continuously self-optimizing emergency response mechanism.

[0061] Taking a real-world operating scenario of an onboard solid-state battery system as an example, the battery module suddenly detects an abnormal signal while the vehicle is in motion. The status monitoring module detects an abnormal voltage fluctuation in module 3 through distributed sensors, with the voltage value dropping from 4.2V to 3.8V within 0.1 seconds. Simultaneously, the infrared thermal imager shows a non-uniform distribution of the surface temperature gradient of the module, with the highest temperature difference reaching 15℃. The electrochemical impedance spectroscopy monitors a significant increase in the electrode interface impedance at the 1000Hz characteristic frequency. The emergency strategy generation module receives the three-dimensional risk evolution model data transmitted by the risk assessment module. The model predicts that the risk of thermal runaway will spread from the current area to adjacent modules within 8 seconds. The module immediately starts calculating the emergency response time window. By analyzing the intersection of the risk diffusion curve and the system safety boundary, the optimal intervention time window is determined to be from the 2nd to the 6th second after the anomaly is detected. This time interval comprehensively considers the mechanical response delay of the actuator (1.2 seconds) and the control signal transmission time (0.3 seconds).

[0062] Based on the emergency response time window, the module optimizes the execution timing of multi-level emergency response strategies. It employs a dynamic programming algorithm to calculate the strategy sequence: at t+2.0 seconds, a local current-limiting control strategy is activated, limiting the output current of module 3 from 200A to 50A; at t+3.5 seconds, a regional isolation control strategy is activated, activating the thermal isolation devices and circuit breakers around module 3; and at t+5.8 seconds, a global power-off control strategy is activated, progressively reducing the output voltage of the entire battery system. This timing arrangement ensures seamless integration of the strategies, avoiding premature intervention that could disrupt normal vehicle operation while preventing delayed responses that could lead to risk contagion.

[0063] When generating an emergency control command sequence containing execution timing, the module improves the timing control precision to the microsecond level: the local current limiting command includes parameters such as a target current value of 50A, a current drop slope of 125A / s, and a duration of 2.8 seconds; the area isolation command specifies the isolators 3-1, 3-2, and 3-3 to be activated, sets a trigger delay of 0.2 seconds, and a minimum sustain time of 4 seconds; the global power-off command includes curve parameters showing the voltage dropping from 400V to 0V in stages, with each 50V drop maintained for 0.5 seconds for stability testing. All commands have nanosecond-level time synchronization markers, and redundant check coding ensures transmission reliability. The command sequence is transmitted to the actuator control module via the CAN bus protocol, and the transmission process uses a priority scheduling mechanism: the local current limiting command is transmitted in real time with the highest priority, the area isolation command with the second highest priority, and the global power-off command with the basic guarantee level. The transmitted data packets contain the command type, execution timestamp, control parameters, and checksum; each data packet is 32 bytes in size, and the transmission rate reaches 1Mbps.

[0064] Throughout the emergency response process, the module continuously receives execution status feedback from the feedback adjustment module: when a local current limiting strategy is implemented and the actual current drop curve deviates from the expected 5%, the module immediately generates a correction command to adjust subsequent strategy parameters; when the area isolation strategy is executed, the pressure sensor confirms that the isolation device's full activation time is 0.1 seconds longer than expected, and the module accordingly adjusts the start time of the global power-off strategy. This demonstrates the complete workflow of the emergency strategy generation module in a real-world scenario: starting with receiving risk model data, through time window calculation, strategy timing optimization, command generation and transmission, an executable emergency control sequence is finally formed. The entire process is completed within milliseconds, reflecting the system's rapid response capability and refined control level to sudden failures.

[0065] See Figure 3 ,Book Figure 3 The complete operation process of the solid-state battery emergency response system when an internal short-circuit anomaly is detected is demonstrated. Figure 3 Divided into four subplots, this diagram illustrates the changes in voltage, temperature, and impedance, as well as the risk level and the implementation of emergency strategies. The top-left subplot shows the battery voltage change over time. Under normal conditions, the battery voltage remains stable at approximately 4.2V. When the system detects an anomaly (approximately 2 seconds later), the voltage rapidly drops from 4.2V to 3.8V within 0.1 seconds; this abrupt voltage change is a typical characteristic of an internal short circuit. The system captures this change using a high-precision voltage sensor at a sampling frequency of 1000 times per second, providing crucial data for subsequent risk assessment. The top-right subplot shows the battery temperature change. Under normal conditions, the battery temperature fluctuates slightly around 25°C. When an anomaly occurs, the temperature begins to rise significantly, with a maximum temperature difference reaching 15°C, indicating that a thermal runaway risk is developing. A distributed thermocouple array monitors temperature changes at different locations on the battery surface and inside, providing the system with temperature gradient distribution data. The bottom-left subplot shows the change in impedance at the electrode-electrolyte interface. Under normal conditions, the impedance value fluctuates around 0.5Ω. When the anomaly occurs, the impedance shows a significant increase at the characteristic frequency of 1000 Hz, indicating an abnormality in the ion migration process. The system utilizes AC impedance spectroscopy to measure the interface impedance characteristics in real time, providing crucial parameters for short-circuit feature extraction. The lower right subplot comprehensively illustrates the changes in risk level and the execution time of emergency strategies. The system calculates the dynamic risk level based on three core characteristic indicators: voltage descent rate, thermal runaway propagation gradient, and ion migration anomaly coefficient. When the voltage descent rate exceeds 50 mV / s, the system marks it as a critical risk state; when the thermal runaway propagation gradient is greater than 10℃ / mm and the ion migration anomaly coefficient is less than 0.7, it is classified as a diffusion risk state; when all three indicators simultaneously exceed the threshold, it is classified as a failure risk state.

[0066] Example 3: The collaborative operation mechanism of the mechanism control module and the feedback adjustment module. This module receives emergency control command sequences from the emergency strategy generation module. These command sequences contain multi-level control commands with precise timestamps and corresponding execution parameters. The command parsing unit inside the module uses a real-time operating system to decode the commands and uses a field-programmable gate array to achieve high-speed command stream processing, converting abstract command parameters into specific drive signals. The control signal of the variable impedance regulator generates an adjustable voltage source output through a digital-to-analog converter. The control command of the phase change material injection device is converted into a pulse width modulation signal to drive the micro pump. The adjustment command of the electromagnetic shielding array generates an electromagnetic field control waveform of a specific frequency through a radio frequency signal generator.

[0067] Driving multiple types of actuators requires differentiated driving strategies based on the physical characteristics of each mechanism. The variable impedance regulator employs a closed-loop control method, dynamically adjusting the impedance value to match the target parameters through real-time monitoring of current feedback, with a response time controlled at the microsecond level. The phase change material injection device uses multi-channel collaborative control, allocating different nozzle injection volume ratios according to the geometric characteristics of the target area, and adjusting the injection flow rate in real time through a pressure sensor. The electromagnetic shielding array uses beamforming technology, achieving directional shielding by adjusting the phase difference of multiple transmitting units, while monitoring the field strength distribution to ensure shielding uniformity. The feedback adjustment module monitors the execution effect of multiple types of actuators in real time. This module collects the actual working status of the actuators through a high-precision sensor network. The actual impedance value of the variable impedance regulator is measured using a four-wire method to eliminate the influence of wiring resistance. The phase change material coverage status is detected by two-dimensional scanning with an infrared thermal imager, and the effective coverage rate is calculated using image processing algorithms. The electromagnetic shielding efficiency is measured in three-dimensional space using a near-field probe array, and a field strength attenuation distribution map is plotted. The collected real-time working parameters are compared with the expected control target, and the deviation values ​​of each parameter are obtained using a differential calculation method and processed according to priority.

[0068] The process of generating dynamic adjustment parameters employs an adaptive control algorithm. The compensation amount is calculated based on the magnitude and direction of the deviation. For impedance adjustment deviation, a proportional-integral controller generates a compensation signal, which is output to the impedance adjustment circuit via a digital-to-analog converter. For material coverage deviation, a fuzzy logic controller adjusts the injection parameters, calculating the required volume of phase change material based on the area of ​​insufficient coverage. For shielding efficiency deviation, a model predictive control algorithm optimizes the field strength distribution, calculating the adjustment amount of the shielding strength using the following formula:

[0069]

[0070] in: This indicates the amount of shielding strength adjustment (unit: Tesla, or T). It is the environmental medium influence coefficient. This represents the real-time monitored electric field strength (unit: volts per meter, or V / m). It is a variable (the unit is seconds, i.e., s), which is used in the integration process from arrive It changes continuously, used to represent every specific moment within the integration range. It is a time decay function. and These represent the start and end times of the adjustment, respectively. It is the vacuum permeability (the unit is Tesla·meter / Ampere, i.e., T·m / A). Wave impedance (unit: ohm, Ω); based on the principle of electromagnetic induction, magnetic field shielding strength , It is the magnetic field strength, and the electric field strength With magnetic field strength satisfy This formula accumulates the historical impact of the electromagnetic field strength change rate through integral calculations, achieving dynamic adjustment of the shielding strength. The actuator control module determines the deployment location of the variable impedance regulator based on the location coordinates and the radius of influence. This process uses graph theory algorithms to analyze the topology of the battery system, calculates the optimal control path centered on the short-circuit point, and selects the node with the highest impedance adjustment sensitivity as the priority control target. Based on the dynamic risk level, the injection flow rate and injection pressure of the phase change material injection device are calculated, and a mapping function between the risk level and fluid parameters is established. The basic parameters are obtained by looking up a table, and then dynamic compensation is performed based on the real-time temperature to ensure that the phase change material achieves the best coverage effect in the designated area.

[0071] Adjusting the shielding strength gradient of the electromagnetic shielding array based on the thermal runaway propagation rate requires establishing a coupled model of the electromagnetic and thermal fields. This model describes the interaction between heat propagation and electromagnetic shielding, dynamically adjusting the spatial distribution of the shielding field strength by monitoring temperature field changes in real time. The shielding strength adjustment employs a gradient descent algorithm, gradually increasing the shielding strength based on the direction of heat propagation to form a suppression gradient in the opposite direction to heat diffusion. The feedback adjustment module collects real-time operating parameters of various actuators, including impedance adjustment accuracy deviation, material coverage difference percentage, and shielding efficiency gap. These parameters are digitally filtered and then sent to the comparator unit for real-time comparison with the preset tolerance range, generating parameter correction commands containing the direction and magnitude of the deviation. Command transmission uses a priority queue mechanism to ensure timely processing of critical deviations. The actuator control module adjusts the drive strategy in real time based on feedback data, while the feedback adjustment module continuously optimizes the monitoring algorithm and correction logic. The two modules exchange data at millisecond levels via a high-speed data bus, using a time-triggered protocol to ensure the real-time and deterministic nature of data transmission. All control commands and feedback data are time-synchronized to ensure the accuracy of collaborative operation of the distributed system.

[0072] Taking a real-world operating scenario of a solid-state battery pack for an electric vehicle as an example, the battery management system detects an anomaly in the second module: the voltage sensor records that the voltage of cell No. 4 drops from 3.65V to 3.25V within 0.2 seconds, the temperature monitoring system shows that the surface temperature gradient of the cell reaches 18℃ / cm, and the impedance monitoring unit detects an abnormal peak in the interface impedance at a frequency of 2000Hz. The actuator control module receives a sequence of instructions from the emergency strategy generation module, which contains three levels of control commands: first, a local current limiting instruction activated 0.5 seconds after the anomaly is detected, requiring the current in the branch containing cell No. 4 to be limited from 150A to 30A; second, a regional isolation instruction activated 1.2 seconds later, specifying the activation of the thermal isolation plate and circuit interrupter around cell No. 4; and finally, a global voltage reduction instruction executed 2.8 seconds later, requiring the voltage of the entire battery pack to be reduced from 600V to a safe voltage in stages. The instruction parsing unit inside the module immediately decodes the received instruction stream and uses a time-slice round-robin scheduling algorithm of the real-time operating system to ensure that high-priority instructions are processed first: local current limiting instructions are converted into analog voltage signals and output to the variable impedance regulator to generate a control voltage of 0-5V corresponding to an impedance adjustment range of 0-200Ω; regional isolation instructions are compiled into digital switch signals and sent to the isolation device controller, specifying the specific relay number and action sequence; global buck instructions are converted into PWM waveforms and output to the main circuit regulator, setting a buck curve with the duty cycle gradually decreasing from 100% to 0%.

[0073] When driving the variable impedance regulator to perform local current limiting control, the module generates precise control signals based on the command parameters: first, it calculates the descent slope from the current of 150A to the target current of 30A and sets it to a gradual change rate of 240A / s; then, it dynamically adjusts the impedance value based on the real-time temperature data of the battery cell, increasing the adjustment by 0.5Ω for every 1°C increase in temperature; simultaneously, it monitors the voltage feedback during the adjustment process to ensure that impedance changes do not cause secondary voltage surges. When controlling the phase change material injection device to perform regional isolation, the module determines the injection strategy based on the thermal distribution map: it calculates a coverage area with a radius of 8cm centered on battery cell No. 4 and allocates the injection ratio of the surrounding 6 nozzles; it calculates the required total amount of phase change material based on the temperature gradient data, sets the injection flow rate to 12ml / s, and maintains the injection pressure at 0.3MPa; it adjusts the injection angle through real-time infrared monitoring to ensure that the material covers the entire hot zone.

[0074] When adjusting the electromagnetic shielding array to implement protective measures, the module generates control parameters based on the thermal runaway propagation model: a gradient distribution of the shielding field strength is set according to the direction of heat propagation, with a shielding strength of 85dB on the high-temperature side and gradually reduced to 60dB on the low-temperature side; the transmission frequency is dynamically adjusted according to real-time temperature changes, gradually increasing from a base of 1.2GHz to 2.4GHz to enhance the shielding effect; simultaneously, the electromagnetic field distribution is monitored to ensure no interference with the normal operation of other sensors in the battery management system. The feedback adjustment module synchronously monitors the actual operating status of each actuator: the actual impedance value of the variable impedance regulator is measured using a high-precision current sensor, and the deviation is found to be within ±0.8Ω when compared with the target value; the actual coverage area of ​​the phase change material is detected using a machine vision system, revealing a coverage rate 5.2% lower than the target value; and the shielding efficiency is measured using an electromagnetic field strength meter, with the measured value 3.7% lower than expected. These monitoring data are uploaded to the control module in real time at a sampling frequency of 100Hz.

[0075] When generating dynamic adjustment parameters, the module employs an adaptive algorithm to calculate compensation: for impedance deviations, it generates a compensation command that increases by 0.2Ω every 0.1 seconds; for insufficient coverage, it calculates the volume of phase change material needed and adjusts the injection parameters of the corresponding nozzles; for shielding efficiency discrepancies, it recalculates the field strength distribution map and optimizes the operating frequency of the transmitting unit. All adjustment parameters are timestamped and prioritized to ensure that critical parameters are adjusted first. Throughout the response process, the module maintains a millisecond-level control loop: it collects the actuator status every 5 milliseconds, generates an adjustment command every 10 milliseconds, and updates the control parameters every 20 milliseconds. This refined control mechanism ensures that emergency response measures are accurately implemented and effectively suppresses the spread of internal short-circuit risks within the solid-state battery.

[0076] Example 4: The feedback adjustment module provides fine monitoring and dynamic adjustment of the actuator's operating status. This module continuously collects real-time operating parameters of the variable impedance regulator, phase change material injection device, and electromagnetic shielding array through a multi-source sensing system. Impedance adjustment accuracy is achieved by using a high-precision bridge measuring instrument to obtain the actual impedance value at a sampling frequency of 2000 times per second. Material coverage is achieved by scanning the distribution of phase change material on the battery surface using a multispectral imaging system. Shielding efficiency is measured by measuring the field strength attenuation ratio before and after shielding using an electromagnetic field strength mapping system. The collected real-time operating parameters are immediately sent to the data preprocessing unit for filtering and standardization. Impedance data undergoes Kalman filtering to eliminate measurement noise, coverage images are processed using edge detection algorithms to extract the effective coverage area, and electromagnetic field data is used to generate a complete three-dimensional field strength distribution map using spatial interpolation. The processed parameters are compared in real-time with the preset expected control target. Impedance adjustment accuracy deviation is calculated using absolute difference, material coverage deviation is analyzed using image pixel difference analysis, and shielding efficiency deviation is compared using the field strength attenuation rate comparison method.

[0077] The process of generating parameter correction commands employs a multi-level decision-making mechanism. For impedance adjustment deviations, when the deviation value exceeds the allowable range, an impedance compensation command is generated, which includes compensation direction, compensation amount, and compensation rate parameters. For material coverage deviations, a directional supplementation command is generated based on the coordinates of the insufficiently covered area, specifying the nozzle number requiring enhanced injection and the supplementary flow rate. For shielding efficiency deviations, a field strength adjustment command is generated, including frequency modulation parameters and power adjustment values. Table 1 illustrates the monitoring and comparison of the actuator's real-time operating parameters.

[0078] Table 1: Real-time Operating Parameter Monitoring Table of the Actuator

[0079]

[0080] The deviation calculation employs a normalization method. Impedance deviation is obtained by dividing the absolute difference between the actual and target values ​​by the measurement range. Material coverage deviation is calculated as a percentage of area difference, and shielding efficiency deviation is determined by the ratio of the actual attenuation rate to the target attenuation rate. The calculated deviation data is compared with preset tolerance thresholds. When any deviation exceeds the tolerance range, the calibration command generation process is immediately triggered. The generated parameter calibration commands are encapsulated in a structured data format. Each command includes a timestamp, device number, calibration type, calibration parameters, and urgency level indicator. Command transmission uses a priority queue mechanism, sending high-urgency calibration commands first to ensure timely processing of critical parameter deviations.

[0081] The feedback adjustment module incorporates historical data learning capabilities, continuously recording the deviation trends and correction effects of various parameters. It optimizes the deviation calculation model and correction parameter generation rules through machine learning algorithms. The module periodically updates tolerance thresholds, dynamically adjusting the allowable deviation range based on the aging of the actuator and the operating environment. The entire implementation process forms a closed-loop control cycle of monitoring, comparison, and correction. A bidirectional data channel is established between the feedback adjustment module and the actuator control module, transmitting monitoring data and correction commands in real time. The module employs a redundant design to ensure the reliability of monitoring data; any sensor failure immediately triggers a backup sensor unit to take over the monitoring task. The execution effect of correction commands is monitored and recorded in real time, generating a correction effect evaluation report. These reports are used to optimize the accuracy of subsequent correction parameter generation, continuously improving the response accuracy and stability of the control system. The module also has self-diagnostic capabilities, capable of identifying systemic deviations caused by actuator performance degradation and providing early warnings of equipment components requiring maintenance or replacement.

[0082] Under complex operating conditions, the feedback adjustment module can coordinate the collaborative correction of multiple actuators. When an interaction is detected between impedance adjustment, material coverage, and shielding efficiency, a joint correction command is generated to ensure that the adjustment operations of each actuator cooperate rather than interfere with each other. This collaborative control capability is achieved through a multivariate optimization algorithm, comprehensively considering the coupling relationship between various parameters and the priority of control objectives. The module's real-time data processing capability supports millisecond-level response, with the entire process from parameter acquisition to command generation time delay controlled within 10 milliseconds, ensuring rapid response to sudden deviations. The data processing unit adopts a parallel computing architecture, processing multiple sensor data simultaneously to ensure the system's operational stability under high load conditions.

[0083] Example 5: This example focuses on the continuous improvement function of the strategy optimization module for the system's emergency response capabilities. This module receives parameter correction instructions and dynamic adjustment parameters from the feedback adjustment module. These data include deviation records and corresponding adjustment logs generated by the actuators during actual operation. The module uses data fusion technology to integrate multi-source information into a time-series dataset, and uses a time alignment algorithm to ensure that the parameter correction instructions and dynamic adjustment parameters are completely synchronized on the time axis. The process of reconstructing the multi-level emergency response strategy adopts a strategy optimization algorithm based on reinforcement learning. The algorithm uses historical execution effect data as a training set to establish a mapping relationship between strategy selection and execution results. The parameter adjustment of the local current limiting control strategy is optimized based on current control accuracy deviation data, the triggering conditions of the regional isolation control strategy are corrected based on isolation effect feedback, and the hierarchical parameters of the global power outage control strategy are recalibrated based on energy loss records. The algorithm continuously updates the node parameters in the strategy decision tree through iterative calculation, making the emergency strategy more closely match the actual operating conditions.

[0084] Establishing an evaluation index system for emergency response effectiveness requires defining quantitative evaluation standards. Risk suppression efficiency is obtained by calculating the ratio of the reduction in risk spread area before and after emergency intervention to the intervention time. This indicator reflects the system's ability to suppress risk spread per unit time. The energy loss coefficient is quantified by measuring the percentage of total energy dissipated during the emergency to the system's total energy storage capacity, reflecting the impact of emergency measures on the system's energy integrity. These two indicators together constitute the core dimension of the evaluation system, providing an objective benchmark for strategy optimization. A multi-objective optimization algorithm is used to calculate the strategy optimization weight coefficients based on risk suppression efficiency and the energy loss coefficient. The algorithm uses Pareto front analysis to find the optimal balance between the two indicators, dynamically allocating weight ratios according to the safety and economic requirements of the actual application scenario. Higher weight coefficients are assigned to risk suppression efficiency in high-risk scenarios, while more emphasis is placed on optimizing the energy loss coefficient under normal operating conditions. The calculated weight coefficients serve as an important basis for strategy adjustment, guiding the subsequent model parameter optimization process.

[0085] Adjusting the parameter thresholds of the three-dimensional risk evolution model based on strategy optimization weight coefficients requires establishing a parameter sensitivity analysis model. First, identify the set of parameters in the model that are most correlated with risk suppression efficiency and energy loss coefficient, including boundary values ​​of thermal conductivity, voltage mutation thresholds, and critical values ​​of ion mobility. Then, recalculate the allowable fluctuation range of these parameters based on the weight coefficients. Under high-risk weight configurations, appropriately tighten the parameter thresholds; under high-economic weight configurations, relax the parameter tolerances. The adjusted parameter thresholds make the risk evolution model more consistent with actual optimization objectives. Updating the control parameter accuracy of the emergency control command sequence involves improving the command generation algorithm. The current control accuracy of local current limiting commands is improved from the milliampere level to the microampere level by increasing the resolution of the digital-to-analog converter to achieve finer current regulation; the time control accuracy is optimized from the millisecond level to the microsecond level by using high-precision clock synchronization technology to ensure the timing accuracy of command execution; the spatial positioning accuracy is improved from the millimeter level to the micrometer level by introducing laser ranging and visual positioning technologies to improve the positioning accuracy of the actuator. These accuracy improvements make emergency control more precise and efficient.

[0086] The strategy optimization module also establishes a long-term learning mechanism, continuously collecting data on strategy execution performance and updating the optimization model. Periodic retraining maintains the strategy's adaptability. The module has a built-in performance evaluation unit that regularly generates optimization performance reports, recording changes in performance metrics before and after optimization, providing a reference for future optimization directions. The entire optimization process forms a closed-loop learning system, enabling continuous improvement in emergency response capabilities with accumulated operational experience. The module employs a distributed computing architecture to process massive amounts of optimization data and utilizes parallel computing technology to accelerate the strategy optimization process, ensuring the system can respond to changes in operational status in real time. Detailed logs are maintained for all optimization operations, including numerical changes before and after parameter adjustments, the calculation process of the optimization algorithm, and the final performance evaluation data. These records provide crucial references for system maintenance and subsequent upgrades. Through continuous strategy optimization, the system gradually builds an emergency response knowledge base adaptable to different operating conditions, continuously improving its ability to handle internal short-circuit faults in solid-state batteries.

[0087] In a specific battery system operation example, the strategy optimization module, through analysis of historical processing data, discovered that when the risk suppression efficiency reached over 85%, the energy loss coefficient often exceeded 12%. The module then adjusted the optimization weights, increasing the weight of risk suppression efficiency from 0.6 to 0.8 and correspondingly decreasing the weight of the energy loss coefficient. This adjustment made subsequent emergency strategies more focused on safety performance, appropriately relaxing constraints on energy loss while ensuring effective risk control. Simultaneously, the module updated the parameter thresholds of the three-dimensional risk evolution model, adjusting the alarm threshold for thermal conductivity from 0.25 W / m·K to 0.22 W / m·K and the voltage surge threshold from 50 mV / s to 45 mV / s, making the model more sensitive to risk signals. These adjustments enabled the system to detect potential risks earlier, allowing more time for emergency response.

[0088] The improved accuracy of the control command sequence is specifically reflected in: the current adjustment step size of the local current limiting command has been reduced from 5mA to 1mA; the time synchronization accuracy has been improved from ±1ms to ±0.1ms; and the positioning error of the actuator has been reduced from ±2mm to ±0.5mm. These improvements make emergency control actions more precise and accurate, reducing additional energy loss caused by coarse control. Through continuous optimization, the system gradually develops an adaptive emergency handling capability for different operating conditions, continuously improving operational efficiency while ensuring safety. The entire optimization process is driven entirely by actual operating data, ensuring that the improvement measures always meet actual needs and avoiding over-optimization or under-optimization.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A short-circuit emergency handling system based on solid-state batteries, characterized in that, include: The status monitoring module is used to collect multi-dimensional operating parameters of the solid-state battery in real time, including voltage fluctuation data, temperature gradient distribution data, and interface impedance change data. The short-circuit feature extraction module is used to perform time-domain and frequency-domain feature analysis on the multi-dimensional operating parameters to identify the core feature indicators of internal short circuits in solid-state batteries. The core feature indicators include voltage descent rate, thermal runaway propagation gradient, and ion migration anomaly coefficient. The risk assessment module is used to calculate the dynamic risk level of internal short circuit in solid-state battery based on the core characteristic indicators. The dynamic risk level includes critical risk state, diffusion risk state and failure risk state. An emergency strategy generation module is used to generate multi-level emergency response strategies based on the dynamic risk level. The multi-level emergency response strategies include local current limiting control strategies, regional isolation control strategies, and global power outage control strategies.

2. The emergency handling system for internal short circuits in solid-state batteries according to claim 1, characterized in that, The short-circuit feature extraction module is also used for: Establish a mapping relationship between the aforementioned core characteristic indicators and the degree of degradation of solid-state battery materials; The location coordinates and radius of influence of the internal short circuit in the solid-state battery are determined based on the mapping relationship. The location coordinates and the radius of influence are transmitted to the risk assessment module.

3. The emergency handling system for internal short circuits in solid-state batteries according to claim 2, characterized in that, The risk assessment module is also used for: Calculate the thermal runaway propagation rate based on the coordinates of the location where the runaway occurred and the radius of the affected area. A three-dimensional risk evolution model is constructed by combining the thermal runaway propagation rate and the core characteristic indicators; The three-dimensional risk evolution model is used to predict the development trajectory of internal short circuits in solid-state batteries.

4. The emergency handling system for internal short circuits in solid-state batteries according to claim 3, characterized in that, The emergency strategy generation module is also used for: The emergency response time window is determined based on the aforementioned three-dimensional risk evolution model; Based on the emergency response time window, the execution sequence of the multi-level emergency response strategy is optimized to generate an emergency control instruction sequence containing the execution sequence.

5. The emergency handling system for internal short circuits in solid-state batteries according to claim 4, characterized in that, Also includes: An actuator control module is used to parse the emergency control command sequence and drive multiple types of actuators, including a variable impedance regulator, a phase change material injection device, and an electromagnetic shielding array. The feedback adjustment module is used to monitor the execution effect of the various types of actuators in real time and generate dynamic adjustment parameters.

6. The emergency handling system for internal short circuits in solid-state batteries according to claim 5, characterized in that, The actuator control module is also used for: The deployment location of the variable impedance regulator is determined based on the coordinates of the occurrence location and the radius of the influence range. Calculate the injection flow rate and injection pressure of the phase change material injection device based on the dynamic risk level; The shielding strength gradient of the electromagnetic shielding array is adjusted according to the thermal runaway propagation rate.

7. The emergency handling system for internal short circuits in solid-state batteries according to claim 6, characterized in that, The feedback adjustment module is also used for: Real-time operating parameters of the various types of actuators are collected, including impedance adjustment accuracy, material coverage, and shielding efficiency. The deviation between the real-time operating parameters and the expected control target is compared, and a parameter correction instruction including the deviation is generated.

8. The emergency handling system for internal short circuits in solid-state batteries according to claim 7, characterized in that, Also includes: The strategy optimization module is used to reconstruct the multi-level emergency response strategy according to the parameter correction instructions and the dynamic adjustment parameters; The strategy optimization module is also used to establish an emergency response effectiveness evaluation index system, which includes risk suppression efficiency and energy loss coefficient.

9. The emergency handling system for internal short circuits in solid-state batteries according to claim 8, characterized in that, The strategy optimization module is also used for: The weighting coefficients are optimized based on the risk suppression efficiency and energy loss coefficient. The parameter thresholds of the three-dimensional risk evolution model are adjusted based on the optimized weight coefficients of the strategy. Update the accuracy of the control parameters in the emergency control command sequence.

10. The emergency handling system for internal short circuits in solid-state batteries according to claim 9, characterized in that, Also includes: The data storage module is used to archive historical data of the multi-dimensional operating parameters, core characteristic indicators, dynamic risk levels, and multi-level emergency response strategies.

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