Lithium battery management method for airplane emergency evacuation power supply guarantee

Abstract

The invention particularly relates to a lithium battery management method for airplane emergency evacuation power supply guarantee, which relates to the technical field of aviation emergency power supplies, and comprises the following steps: acquiring a plurality of electrical parameter signals and acoustic emission signals in a battery pack, inspecting the acoustic emission signals, and fusing the characteristic parameters of the electrical signals and the acoustic emission signals to obtain a lithium battery pack; and carrying out cross validation on the health state and the internal short circuit fault of the battery cell. According to the invention, multi-mode sensing fusion of electrical parameters and acoustic emission signals is combined with a four-layer acoustic emission signal inspection mechanism, so that the limitation that a traditional battery management system depends on a single electrical signal is broken through; on one hand, microscopic faults such as lithium dendrite growth and internal short circuit in the battery cell can be accurately captured; on the other hand, on the basis of a D-S evidence theory or a fuzzy logic fusion algorithm, multi-source evidence weights are dynamically distributed, battery health decline can be early warned in advance, it is ensured that the lithium battery can be instantly started and stably supplies power under the extreme working condition when the airplane emergently evacuates, and the high reliability requirement of the aviation field for a power source is met.

Description

Technical Field

[0001] This invention relates to the field of aviation emergency power technology, and in particular to a lithium battery management method for ensuring power supply during aircraft emergency evacuation. Background Technology

[0002] In the aviation field, emergency evacuation systems (such as emergency lighting, slide inflation, and crew guidance systems) are core components for ensuring passenger safety in flight accidents. The stability of their power supply directly determines evacuation efficiency and the probability of personnel survival. Lithium-ion batteries, due to their high energy density and high power output characteristics, have become the preferred energy storage medium for aircraft emergency power. However, the extreme operating conditions in aircraft emergency scenarios pose severe challenges to lithium-ion battery management: In emergency situations, lithium-ion batteries need to start up instantly and provide stable power in complex environments such as aircraft power failure, structural impact, cabin smoke, and extreme temperatures of -40℃ to 85℃. At the same time, they need to avoid the risk of short circuits and thermal runaway within the cells caused by collisions. Furthermore, long-term float charging storage can easily lead to cumulative errors in traditional state of charge (SOC) estimation, which may result in the inability to release the rated power in an emergency.

[0003] Current mainstream lithium battery management systems (BMS) are mostly designed for conventional energy storage or automotive scenarios, making it difficult to adapt to the special needs of emergency power supply in aircraft.

[0004] On the one hand, traditional BMS relies on a single electrical parameter (voltage, current, temperature) for status diagnosis, which is prone to insufficient reliability due to sensor failure or algorithm misjudgment, and cannot meet power supply requirements;

[0005] On the other hand, existing systems use fixed protection thresholds, making it impossible to quickly switch between normal maintenance modes that ensure lifespan and emergency modes that release full energy. Furthermore, they lack early warning and active blocking mechanisms for thermal runaway, making it difficult to cope with thermal safety risks in the confined space of the cabin.

[0006] Therefore, a lithium battery management method is needed for emergency power supply during aircraft evacuation to address the aforementioned issues. Summary of the Invention

[0008] The purpose of this invention is to provide a lithium battery management method for emergency power supply during aircraft evacuation in order to solve the above-mentioned problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] Lithium battery management methods for emergency power supply during aircraft evacuation include:

[0011] Step 1: Acquire multiple electrical parameter signals and acoustic emission signals within the battery pack. After verifying the acoustic emission signals, fuse the characteristic parameters of the electrical signals and acoustic emission signals to cross-verify and comprehensively diagnose the health status and internal short-circuit faults of the battery cells.

[0012] Step 2: Set normal thresholds and emergency thresholds, and switch the thresholds accordingly based on the received emergency trigger signals;

[0013] Step 3: Deeply integrate thermal management into the balancing strategy to achieve unified optimization of energy management and thermal safety;

[0014] Step 4: Based on the results of multi-parameter fusion analysis, obtain the probability of thermal runaway risk for early warning.

[0015] Preferably, step one specifically includes:

[0016] Electrical parameter signals include the voltage, current, and temperature signals of each cell in the battery pack;

[0017] A broadband acoustic emission sensor is installed tightly inside each battery module to collect acoustic emission signals, and the acoustic emission signals are verified to ensure signal accuracy.

[0018] A fusion algorithm based on DS evidence theory is constructed; the internal resistance growth rate, capacity decay rate and acoustic emission characteristic parameter change rate are used as input evidence; the algorithm assigns a dynamic confidence weight to each piece of evidence and finally outputs the battery health state (SOH) estimate.

[0019] Real-time monitoring of voltage difference and temperature rise rate, and triggering corresponding primary alarms;

[0020] When the primary alarm is triggered, immediately retrieve the acoustic emission signals of the battery cell within a preset time period before and after the incident:

[0021] If a continuous high-frequency, high-energy event is detected in the acoustic emission signal, and its characteristics match the preset internal short-circuit acoustic fingerprint database, an internal short-circuit fault is confirmed, triggering the highest level alarm.

[0022] Preferably, the specific process of verifying the acoustic emission signal to ensure signal accuracy includes:

[0023] A1: Before the acoustic emission signal is digitized by the acquisition card, interference is physically filtered out using analog circuit hardware;

[0024] A2: Calculate the digitized discrete signal, extract the characteristic parameters that characterize the essence of the acoustic emission event, and perform initial screening based on the preset physical event library;

[0025] A3: Utilizing the principles of wave propagation physics, through the collaboration of multiple sensors, it distinguishes between real events occurring inside the battery and interference from the external environment;

[0026] A4: By spatiotemporally correlating isolated evidence of acoustic emission events with the electrochemical response of the battery, and following the principle of causality, a final judgment can be made.

[0027] Preferably, the specific content of A2 includes:

[0028] Define an acoustic emission Hit: When a signal waveform exceeds a preset trigger threshold, recording begins and continues until the waveform falls below the threshold again and remains below it for a preset duration. The entire process is then recorded as a Hit event.

[0029] Calculate the following parameters:

[0030] Rise time: The time interval from when the waveform first crosses the threshold point to when it reaches the peak point of the waveform;

[0031] Duration: The total time from the first crossing of the threshold to the final drop below the threshold;

[0032] Ring count: The total number of times the waveform crosses the threshold within the specified duration.

[0033] Amplitude: The maximum absolute value of the voltage signal throughout the entire Hit event;

[0034] Absolute energy: The result obtained by squaring the voltage signal over the entire Hit period and then integrating it over time;

[0035] Battery samples were tested, and acoustic emission signals were collected to establish a database of acoustic emission parameters under different fault modes, and rules for eliminating false signals were set.

[0036] Preferably, the specific content of A3 includes:

[0037] At least three acoustic emission sensors are arranged non-collinearly on each battery module. , , ), forming a measurement array;

[0038] All sensor data acquisition is driven by the same clock source, and cross-correlation algorithms are used to calculate the arrival of the same event signal. and Time difference and arrival and Time difference The cross-correlation algorithm can effectively overcome noise and find the time of arrival more accurately than the simple thresholding method.

[0039] The preset speed of sound wave propagation in battery materials is Establish a system of hyperbolic equations and solve for the coordinates of the sound source. ;

[0040] A three-dimensional virtual boundary is defined for each battery module, based on the calculated sound source coordinates. Located within the virtual boundary of its associated cell or module, thus:

[0041] If the calculated sound source location is clearly outside the module boundary, it is determined to be an external impact or vibration and is immediately eliminated;

[0042] Set a minimum time difference threshold; if the electromagnetic interference (EMI) signal arrives at each sensor within a certain time difference... If the time difference is less than the minimum time difference threshold, it is judged as EMI and rejected.

[0043] Preferably, the specific content of A4 includes:

[0044] Add a unified timestamp to all data;

[0045] A real-world internal battery event is the starting point of a causal chain, and its logic is as follows:

[0046] Timing: A high-energy event that matches the characteristics and is located inside the battery, captured by the acoustic emission system, is recorded as an event. ;

[0047] + Timing: Due to an internal short circuit causing a change in the internal resistance of the corresponding cell, the voltage monitoring process should be able to detect an abnormal sharp drop in the cell voltage, which is recorded as an event. ;

[0048] + Timing: Due to ohmic heat generation at the internal short circuit point, the temperature sensor should be able to detect an abnormally significant increase in the rate of temperature rise in the cell or nearby area, which is recorded as an event. ;

[0049] Create a time window within which events occur sequentially. , , This confirms the event as a genuine malfunction and triggers the highest level of alert.

[0050] Preferably, step two specifically includes:

[0051] Thresholds are divided into normal maintenance mode thresholds and emergency release mode thresholds;

[0052] Emergency trigger signals from the aircraft's core avionics system are received directly via a dedicated hardwired signal; once this signal is true, the BMS master controller completes the mode switch.

[0053] Immediately release the protection command based on the normal maintenance threshold;

[0054] Load the emergency release mode threshold library;

[0055] The mode switching command is sent synchronously to all corresponding parts.

[0056] Preferably, step three specifically includes:

[0057] An active balancing circuit is constructed using a flying capacitor or a multi-winding transformer.

[0058] A simplified thermal model is built for each cell, with the cell's real-time current as the input. and current temperature The output is a temperature rise prediction within a preset future time period. ;

[0059] When balancing is required, the BMS master controller simulates and calculates all possible balancing paths.

[0060] For each path, the model predicts the module's maximum temperature during the equalization process due to energy transfer efficiency losses and the equalization current flowing through internal resistance. Maximum temperature difference of the module ;

[0061] And select one that makes the objective function The minimized equilibrium path, where and These are the weighting coefficients.

[0062] Preferably, step four specifically includes:

[0063] The gas sensor specifically detects carbon monoxide and hydrogen; the integrated micro-differential pressure sensor monitors minute changes in the internal pressure of the module.

[0064] Construct an early warning model based on a long short-term memory network; the model inputs include: voltage, temperature, rate of temperature change, rate of voltage change, acoustic emission eigenvalues, rate of change of carbon monoxide concentration, rate of change of hydrogen concentration, and rate of change of pressure.

[0065] The model's decision layer outputs the probability of thermal runaway risk.

[0066] Preferably, the method further includes:

[0067] Step 5: During emergency discharge, continuously record key operating parameters of the battery system to an independently powered non-volatile memory; and after the event ends, automatically analyze the recorded data to generate a health status report that includes total energy released, cell consistency performance, and maintenance recommendations.

[0068] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0069] 1. This invention overcomes the limitations of traditional battery management systems that rely on a single electrical signal by fusing multimodal perception of electrical parameters and acoustic emission signals, combined with a four-layer acoustic emission signal verification mechanism. On the one hand, it can accurately capture microscopic faults such as lithium dendrite growth and internal short circuits inside the battery cell. On the other hand, based on the fusion algorithm of DS evidence theory or fuzzy logic, it dynamically allocates the weights of multi-source evidence, which can provide early warning of battery health degradation and ensure that the lithium battery can start up instantly and provide stable power supply under extreme conditions during emergency evacuation of the aircraft, meeting the high reliability requirements of the aviation field for power supply.

[0070] 2. This invention resolves the contradiction between emergency full energy release and thermal safety protection through a thermoelectric coupling model and a graded thermal runaway prevention mechanism. The thermoelectric coupling balancing strategy, when the cell energy is balanced, calculates and optimizes the thermal risks of all balancing paths through simulation, so that SOC consistency and thermal risk minimization are achieved simultaneously. Step four, with its multi-parameter fusion of thermal runaway early warning and graded prevention, can proactively curb the spread of thermal runaway and avoid catastrophic consequences in a confined cabin. At the same time, the dynamic threshold switching in step two temporarily relaxes protection parameters to release all available energy in an emergency, and the parameters are verified for safety, ensuring the high-power power supply continuity of the emergency load and achieving proactive thermal safety protection throughout the entire process from energy management to fault handling. Attached Figure Description

[0071] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0072] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0073] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0074] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0075] Example 1

[0076] Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0077] Appendix Figure 1 The flowchart of the lithium battery management method for emergency evacuation power supply provided in the embodiments of the present invention shows the complete steps from acquiring multiple electrical parameter signals and acoustic emission signals in the battery pack to obtaining the probability of thermal runaway risk based on the multi-parameter fusion analysis results for early warning.

[0078] In this embodiment, it includes:

[0079] Step 1: Collaborative diagnosis of battery status based on multimodal sensing fusion: acquire multiple electrical parameter signals and acoustic emission signals in the battery pack, and after verifying the acoustic emission signals, fuse the characteristic parameters of the electrical signals and acoustic emission signals to cross-verify and comprehensively diagnose the health status and internal short circuit faults of the battery cells;

[0080] Specifically, it includes:

[0081] Electrical parameter signals include the voltage, current, and temperature signals of each cell in the battery pack;

[0082] A high-precision, high-sampling-rate analog-to-digital converter (ADC) is used to synchronously acquire the terminal voltage, loop current, and temperature values ​​of at least three key points (positive electrode, negative electrode, and sidewall) of each cell in the battery pack at a frequency of no less than 100Hz. The voltage measurement accuracy is better than ±1mV, the current measurement accuracy is better than ±0.5%, and the temperature measurement accuracy is better than ±0.5℃.

[0083] A wideband acoustic emission sensor (frequency response range 50kHz-500kHz) is tightly fitted inside each battery module to monitor microscopic stress wave signals generated inside the cell due to faults such as lithium dendrite growth, separator tearing, and internal short circuits. Sensor data is collected at a sampling rate of no less than 1MHz to obtain acoustic emission signals, and the signals are verified to ensure accuracy.

[0084] A fusion algorithm based on DS evidence theory or fuzzy logic is constructed; the internal resistance growth rate, capacity decay rate (from periodic full capacity calibration), and acoustic emission characteristic parameter change rate are used as input evidence; the algorithm assigns a dynamic confidence weight to each piece of evidence (e.g., the weight of acoustic emission evidence can be increased in the later stages of the cycle), and finally outputs a comprehensive, high-confidence estimate of the battery state of health (SOH).

[0085] Real-time monitoring of voltage difference ( ) and rate of temperature rise ( ), and trigger the corresponding primary alarm (if the voltage of any cell drops by more than 50mV instantaneously or If the speed is greater than 1℃ / s, a primary alarm will be triggered.

[0086] When the primary alarm is triggered, immediately retrieve the acoustic emission signals of the battery cell within a preset time period before and after the incident:

[0087] If a continuous high-frequency, high-energy event is detected in the acoustic emission signal, and its characteristics match the preset internal short-circuit acoustic fingerprint database, then an internal short-circuit fault is confirmed, and the highest level alarm is triggered.

[0088] If there is no supporting acoustic emission characteristics, it may be determined that the connector is loose or the sensor is faulty, triggering a mid-level maintenance alarm.

[0089] The specific process of verifying acoustic emission signals to ensure their accuracy includes:

[0090] A1: Before the acoustic emission signal is digitized by the acquisition card, analog circuit hardware is used to physically filter out the most widespread interference that does not overlap with the characteristic frequency band of acoustic emission.

[0091] Specific implementation of high-frequency bandpass filters:

[0092] Active analog filters with steep roll-off characteristics (such as 24 dB or higher per octave) are used, and their core operational amplifiers must be low-noise and have a wide temperature range to adapt to the aviation environment.

[0093] Passband setting: Passband set to 150kHz to 400kHz.

[0094] Lower limit 150kHz: designed to completely filter out the most important low-frequency structural vibration noise on the aircraft (such as engine vibration, airflow disturbance, noise generated by landing gear retraction and extension, usually <50kHz) and electrical noise (50 / 60Hz power frequency and its harmonics).

[0095] Upper limit 400kHz: Designed to filter out high-frequency electromagnetic interference while ensuring that the main acoustic emission energy generated by internal battery events (such as lithium dendrite fracture and particle crack propagation) is captured, most of which are concentrated in this frequency band.

[0096] Installation location: The filter module should be highly integrated and placed as close as possible to the acoustic emission sensor (e.g., inside the sensor housing or on an adjacent terminal block), following the principle of proximity to prevent long wires from introducing new interference.

[0097] The preamplifier's gain strategy is as follows: When the system is initialized or the background noise is low, a lower gain is used; when a possible fault is detected, the signal is enhanced, or the system self-test detects a decrease in the signal-to-noise ratio, the gain is automatically switched to a high gain level to dynamically adapt to the signal strength and avoid signal saturation or overwhelming.

[0098] A2: Calculate the digitized discrete signal, extract the characteristic parameters that can characterize the essence of the acoustic emission event, and perform initial intelligent screening based on the preset physical event library;

[0099] The specific content includes:

[0100] Define an acoustic emission Hit: When a signal waveform exceeds a preset trigger threshold, recording begins until the waveform falls below the threshold again and remains below it for a preset duration. The entire process is recorded as a Hit event.

[0101] Calculate the following parameters:

[0102] Rise time: The time interval from when the waveform first crosses the threshold point to when it reaches the peak point; it reflects the suddenness of the event. Events such as internal short circuits have extremely short rise times (in microseconds).

[0103] Duration: The total time from the first crossing of the threshold to the final drop below the threshold; Physical meaning: Continuous vibration or friction noise usually lasts for a long time;

[0104] Ring count: The total number of times a waveform crosses a threshold over a given duration; related to the energy of the event and the damping properties of the material.

[0105] Amplitude: The maximum absolute value of the voltage signal (in dB) throughout the entire Hit event. This is one of the most important parameters for measuring the intensity of the event;

[0106] Absolute energy: The result obtained by squaring the voltage signal over the entire Hit period and then integrating it over time; it directly reflects the amount of mechanical energy released by the event and is a better characterizer of the severity of the event than the amplitude.

[0107] By conducting accelerated aging, nail penetration, overcharge and over-discharge tests on battery samples in the laboratory, and simultaneously collecting acoustic emission signals, a database of acoustic emission parameters under different fault modes was established.

[0108] For example:

[0109] Characteristic fingerprints of internal micro-short circuit events: high amplitude (>80dB), high energy, short rise time (<5μs), and short duration.

[0110] Characteristic fingerprints of lithium dendrite growth events: low to medium amplitude, continuous signal, and high ringing count.

[0111] And establish rules for rejecting false signals:

[0112] Establish energy and amplitude thresholds: any event below these thresholds is considered background noise and is ignored.

[0113] Establish a duration threshold: Any event whose duration exceeds the threshold is considered inconsistent with the physical process of rapid failure inside the battery and is likely to be caused by external vibration, and is therefore excluded;

[0114] Morphological screening: Identifying and filtering continuous events with highly periodic or completely uniform patterns through algorithms, which are usually caused by electrical noise or mechanical friction.

[0115] A3: By utilizing the principles of wave propagation physics and through the collaboration of multiple sensors, we can fundamentally distinguish between real events occurring inside the battery and interference from the external environment.

[0116] The specific content includes:

[0117] Optimized arrangement of sensor array:

[0118] At least three acoustic emission sensors are arranged non-collinearly on each battery module. , , This forms a measurement array; the sensor is tightly connected to the surface of the battery cell housing using a waveguide rod or directly through high-temperature coupling adhesive to ensure efficient transmission of sound waves.

[0119] All sensor data acquisition is driven by the same clock source, and the time synchronization accuracy is required to reach the nanosecond level, which is a prerequisite for achieving precise positioning.

[0120] The cross-correlation algorithm is used to calculate the arrival of signals for the same event. and Time difference and arrival and Time difference The cross-correlation algorithm can effectively overcome noise and find the time of arrival more accurately than the simple thresholding method.

[0121] The preset speed of sound wave propagation in battery materials is (This can be pre-calibrated experimentally), establish a system of hyperbolic equations, and solve for the coordinates of the sound source. ;

[0122] Construct a system of hyperbolic equations:

[0123] Set sensor , , The coordinates are , , The coordinates of the sound source are ;

[0124] Sound waves from the sound source to The propagation time is ,arrive The propagation time is ,arrive The propagation time is ,but:

[0125]

[0126] Due to time difference = , = , eliminate , , After that, one can obtain , The equation of a hyperbola with focus ( ,in , For the sound source to , (distance), and with , The equation of the hyperbola with focus 4;

[0127] The intersection of two hyperbolas (hyperboloids in 3D) combined with the spatial constraints of the battery module (such as the sound source should be inside the cell) ultimately uniquely determines the coordinates of the sound source. ;

[0128] A three-dimensional virtual boundary is defined for each battery module, based on the calculated sound source coordinates. Located within the virtual boundary of its associated cell or module, thus:

[0129] If the calculated sound source location is clearly outside the module boundary (e.g., on the mounting bracket or cabin wall), it is determined to be an external impact or vibration and is immediately eliminated;

[0130] Electromagnetic interference (EMI) signals travel at the speed of light and arrive at all sensors almost simultaneously, resulting in calculated time differences. Nearly zero; set a minimum time difference threshold, if the time difference between the arrival of the electromagnetic interference (EMI) signal at each sensor is close to zero. If the time difference is less than the minimum time difference threshold, it is judged as EMI and rejected.

[0131] The sound source of a real acoustic emission event is inside the battery, and the distance to different sensors varies. The time difference is necessarily greater than the tiny time difference caused by the propagation of light at the speed of light. Therefore, the magnitude of the time difference can be used to efficiently distinguish between EMI and real acoustic emission.

[0132] A4: By spatiotemporally correlating isolated evidence of acoustic emission events with the electrochemical response of the battery, and following the principle of causality, a final judgment can be made, minimizing false alarms.

[0133] The specific content includes:

[0134] The acoustic emission acquisition system, voltage / current acquisition circuit, and temperature monitoring are all connected to a unified precision timing source, giving all data a unified and accurate microsecond-level timestamp.

[0135] Constructing a causal relationship model:

[0136] A real internal battery event (such as an internal short circuit) is the starting point of a causal chain, the logic of which is as follows:

[0137] Timing: A high-energy event that matches the characteristics and is located inside the battery, captured by the acoustic emission system, is recorded as an event. ;

[0138] + Timing: Due to an internal short circuit causing a change in the internal resistance of the corresponding cell, the voltage monitoring process should be able to detect an abnormal sharp drop in the cell voltage, which is recorded as an event. ;

[0139] + Timing: Due to the ohmic heat generated at the internal short circuit point, the temperature sensor should be able to detect abnormal temperature rise rates in or near the cell. A significant increase is recorded as an event. ;

[0140] Create a time window within which events occur sequentially. , , This confirms the event as a genuine malfunction and triggers the highest level of alert.

[0141] If an acoustic emission event After it occurs, within a preset time window, the voltage and temperature If the curves show no abnormal changes, the acoustic emission event is determined to be a false alarm and is not accepted. Even if it passes all the previous screening layers, it will be ultimately rejected due to a lack of supporting electrochemical evidence.

[0142] Step 2: Dynamically adjustable threshold management for emergency situations: Set normal thresholds and emergency thresholds, and switch the thresholds accordingly based on the received emergency trigger signals;

[0143] Specifically, it includes:

[0144] Establishment of a dual-mode threshold library:

[0145] Normal maintenance mode thresholds: With the goal of improving battery cycle life, relatively strict protection parameters are set; for example: upper voltage limit 4.15V, lower voltage limit 3.2V, upper charging current limit 0.5C, upper discharging current limit 1C, and upper temperature limit 50℃.

[0146] Emergency release mode thresholds: With the primary goal of releasing all available energy and ensuring the core load, set lenient parameters that have been verified for safety; for example: temporarily relax the lower voltage limit to 2.2V (based on the deep discharge characteristic curve of the battery cell to ensure that it will not reverse charge), allow the discharge current to reach 3C or above for a short time (such as 3 minutes), and temporarily relax the upper temperature limit to 70℃ (based on sufficient margin for thermal runaway trigger temperature).

[0147] Emergency trigger signals from the aircraft's core avionics system are received directly via dedicated hardwired signals (such as 28VDC); these emergency trigger signals have the highest priority and are not affected by software logic.

[0148] Once this signal is true, the BMS master controller will complete the mode switch within milliseconds:

[0149] Immediately release the protection command based on the normal maintenance threshold;

[0150] Load the emergency release mode threshold library to allow the battery to release all available energy to meet the high power requirements of emergency loads.

[0151] The system synchronously sends mode switching commands to all corresponding components (such as battery cell modules, equalization modules, thermal management modules, etc.) to ensure that the entire system enters emergency mode in a coordinated manner.

[0152] In this mode, the system retains only the most critical protections (such as short-circuit protection for the entire battery pack). For over-discharge and mild over-temperature of individual cells, it only records and alarms, but does not execute the command to cut off the output, thus ensuring the continuity of power supply to emergency loads.

[0153] Step 3: Intelligent Equilibrium and Thermal Management Based on Thermal-Electrical Coupling Model: Deeply integrate thermal management into the equilibrium strategy to achieve unified optimization of energy management and thermal safety;

[0154] Specifically, it includes:

[0155] An active balancing circuit composed of a flying capacitor or a multi-winding transformer is used; its balancing current can reach 1A-5A, which is much higher than the 100mA level of passive balancing, and can quickly transfer energy between cells.

[0156] Thermal-electric coupling model and optimization objective function:

[0157] A simplified thermal model (such as a second-order RC network thermal model) is established for each battery cell, with the real-time current of that cell as its input. and current temperature The output is a temperature rise prediction within a preset future time period. ;

[0158] When balancing is required (e.g., SOC difference > 3%), the BMS main controller no longer simply finds the cell with the highest charge to transfer energy to the cell with the lowest charge, but instead simulates and calculates all possible balancing paths (e.g., A->B, A->C, B->C...).

[0159] For each path, the model predicts the highest module temperature during the equalization process due to energy transfer efficiency losses (heat generation) and equalization current flowing through internal resistance (heat generation). Maximum temperature difference of the module ;

[0160] And select one that makes the objective function The minimized equilibrium path, where and This is a weighting coefficient, which can be dynamically adjusted according to the current ambient temperature (e.g., increase when the ambient temperature is high). The weighting of the parameters prioritizes suppressing the highest temperature. This allows the system to proactively minimize thermal risks while achieving SOC consistency.

[0161] Step 4: Early warning and active blocking of thermal runaway: Based on the multi-parameter fusion analysis results of cell voltage, temperature, temperature change rate, acoustic emission signal, characteristic gas concentration and pressure change, the probability of thermal runaway risk is obtained for early warning; and after the warning is confirmed, active blocking measures such as local cooling and controllable pressure relief are initiated for the faulty cell.

[0162] Specifically, it includes:

[0163] Integrate miniaturized gas sensors (such as MEMS technology) at the module level to specifically detect carbon monoxide and hydrogen (characteristic gases in the early stages of thermal runaway); integrate micro differential pressure sensors to monitor minute changes in internal pressure of the module.

[0164] Construct an early warning model based on a Long Short-Term Memory (LSTM) network; the model's input is a multi-dimensional time series, including:

[0165] Voltage: Electrical parameters such as the terminal voltage of a battery cell or module, reflecting the battery's potential state;

[0166] Temperature: The temperature of the battery cell, module, or surrounding environment is a core parameter for thermal management and thermal runaway monitoring;

[0167] Temperature change rate: The rate at which temperature changes over time, used to capture the rapid temperature rise trend in the early stages of thermal runaway;

[0168] Voltage change rate: The rate at which voltage changes over time, which can reflect abnormalities in the internal electrochemical processes of the battery (such as rapid voltage fluctuations during an internal short circuit).

[0169] Acoustic emission characteristic values: After processing the raw signal (stress wave signal for monitoring micro-faults inside the battery) collected by the acoustic emission sensor, characteristic parameters such as energy value, amplitude, event count, and rise time are extracted.

[0170] Carbon monoxide concentration change rate: The rate at which the concentration of carbon monoxide (CO) changes over time. CO is a characteristic gas released in the early stages of battery thermal runaway, and its concentration change rate can help to provide early warning of thermal runaway.

[0171] Hydrogen concentration change rate: The rate at which hydrogen concentration changes over time. Hydrogen is also a characteristic gas in the early stages of thermal runaway, and its concentration change is used to assess the risk of thermal runaway.

[0172] Pressure change rate: The rate at which the internal pressure of the battery module changes over time. During thermal runaway, gas production inside the battery will cause the pressure to rise, and this parameter can reflect this trend in the early stages.

[0173] The model outputs a thermal runaway risk probability from 0 to 1. When the thermal runaway risk probability exceeds the preset threshold in multiple consecutive sampling points, it is determined that the irreversible stage of thermal runaway has begun and an early warning is issued (several minutes earlier than open flame or violent temperature rise).

[0174] Tiered active blocking mechanism:

[0175] Level 1 Response (when the warning is confirmed): Immediately disconnect the charging circuit of the module where the faulty cell is located, start the independent microchannel cooling system of the module (the pump drives the insulating coolant to flow through the microchannels inside the module) or activate the phase change material (PCM) around it to provide powerful heat dissipation.

[0176] Level 2 Response (when risk continues to escalate): Triggers a physical blocking device. This device can be a shape memory alloy (SMA) based needle that recovers its shape after being heated by electricity, piercing the casing of the faulty battery cell; or it can be a miniature rupture disc that detonates via an electrical signal. The purpose is to release the pressure and flammable gas accumulated inside the battery cell within a controllable range, relieving pressure rather than causing an explosion.

[0177] Level 3 response (isolation): Electrically isolates the faulty module from the entire battery system within milliseconds using relays or pyro-fuse to prevent the disaster from escalating.

[0178] Step 5: Emergency Discharge Process Recording and Self-Check: During the emergency discharge process, key operating parameters of the battery system are continuously recorded to an independently powered non-volatile memory; and after the event ends, the recorded data is automatically analyzed to generate a health status report including total energy released, cell consistency performance, and maintenance recommendations, specifically including:

[0179] The system is equipped with an independent power supply (such as a supercapacitor) SRAM or FRAM chip as a data black box.

[0180] From the moment the emergency trigger signal arrives, the system records the following data at the highest sampling rate (e.g., 10Hz for voltage and current, 1Hz for temperature, and 1Hz for critical status flags): timestamp, total voltage, total current, voltage of each cell, temperature of each cell, balance status, amount of electricity discharged, warning event flag, and triggered protection actions.

[0181] Data storage employs a cyclic overwrite mechanism, but data segments related to emergency events are marked for permanent storage to prevent them from being overwritten by subsequent data.

[0182] After the incident, the system's main controller automatically runs a self-diagnostic algorithm to analyze the black box data and generate a structured report, which includes:

[0183] Performance data: Total discharge duration, total released energy (kWh), maximum discharge rate, and minimum voltage of this emergency event.

[0184] Health assessment: maximum voltage difference between cells at the end of discharge, highest temperature and temperature rise, and consistency evaluation.

[0185] Event log: Whether any warnings or protections were triggered (e.g., whether emergency mode was entered, whether over-temperature alarms were recorded).

[0186] Maintenance Recommendations: Based on the data from this high-rate discharge, recalibrate the State of Charge (SOC) and State of Harshness (SOH), and provide clear recommendations on whether the system can continue to be used, requires in-depth inspection, or should be replaced immediately. Ground staff can quickly access this report via a physical interface (such as USB or maintenance Ethernet port).

[0187] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0188] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0189] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0190] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0191] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0192] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0193] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0195] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0196] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A lithium battery management method for emergency power supply during aircraft evacuation, characterized in that, include: Step 1: Acquire multiple electrical parameter signals and acoustic emission signals within the battery pack. After verifying the acoustic emission signals, fuse the characteristic parameters of the electrical signals and acoustic emission signals to cross-verify and comprehensively diagnose the health status and internal short-circuit faults of the battery cells. Step 2: Set normal thresholds and emergency thresholds, and switch the thresholds accordingly based on the received emergency trigger signals; Step 3: Deeply integrate thermal management into the balancing strategy to achieve unified optimization of energy management and thermal safety; Step 4: Based on the results of multi-parameter fusion analysis, obtain the probability of thermal runaway risk for early warning.

2. The lithium battery management method for emergency power supply guarantee during aircraft evacuation according to claim 1, characterized in that, Step one specifically includes: Electrical parameter signals include the voltage, current, and temperature signals of each cell in the battery pack; A broadband acoustic emission sensor is installed tightly inside each battery module to collect acoustic emission signals, and the acoustic emission signals are verified to ensure signal accuracy. A fusion algorithm based on DS evidence theory is constructed; the internal resistance growth rate, capacity decay rate, and acoustic emission characteristic parameter change rate are used as input evidence; the algorithm assigns a dynamic credibility weight to each piece of evidence and finally outputs the battery health state (SOH) estimate. Real-time monitoring of voltage difference and temperature rise rate, and triggering corresponding primary alarms; When the primary alarm is triggered, immediately retrieve the acoustic emission signals of the battery cell within a preset time period before and after the incident: If a continuous high-frequency, high-energy event is detected in the acoustic emission signal, and its characteristics match the preset internal short-circuit acoustic fingerprint database, an internal short-circuit fault is confirmed, triggering the highest level alarm.

3. The lithium battery management method for emergency power supply during aircraft evacuation according to claim 2, characterized in that, The specific process of verifying acoustic emission signals to ensure their accuracy includes: A1: Before the acoustic emission signal is digitized by the acquisition card, interference is physically filtered out using analog circuit hardware; A2: Calculate the digitized discrete signal, extract the characteristic parameters that characterize the essence of the acoustic emission event, and perform initial screening based on the preset physical event library; A3: Utilizing the principles of wave propagation physics, through the collaboration of multiple sensors, it distinguishes between real events occurring inside the battery and interference from the external environment; A4: By spatiotemporally correlating isolated evidence of acoustic emission events with the electrochemical response of the battery, and following the principle of causality, a final judgment can be made.

4. The lithium battery management method for emergency power supply guarantee during aircraft evacuation according to claim 3, characterized in that, The specific content of A2 includes: Define an acoustic emission Hit: When a signal waveform exceeds a preset trigger threshold, recording begins and continues until the waveform falls below the threshold again and remains below it for a preset duration. The entire process is then recorded as a Hit event. Calculate the following parameters: Rise time: The time interval from when the waveform first crosses the threshold point to when it reaches the peak point of the waveform; Duration: The total time from the first crossing of the threshold to the final drop below the threshold; Ring count: The total number of times the waveform crosses the threshold within the specified duration. Amplitude: The maximum absolute value of the voltage signal throughout the entire Hit event; Absolute energy: The result obtained by squaring the voltage signal over the entire Hit period and then integrating it over time; Battery samples were tested, and acoustic emission signals were collected to establish a database of acoustic emission parameters under different fault modes, and rules for eliminating false signals were set.

5. The lithium battery management method for emergency power supply guarantee during aircraft evacuation according to claim 4, characterized in that, The specific content of A3 includes: At least three acoustic emission sensors are arranged non-collinearly on each battery module. , , ), forming a measurement array; All sensor data acquisition is driven by the same clock source, and cross-correlation algorithms are used to calculate the arrival of the same event signal. and Time difference and arrival and Time difference The cross-correlation algorithm can effectively overcome noise and find the time of arrival more accurately than the simple thresholding method. The preset speed of sound wave propagation in battery materials is Establish a system of hyperbolic equations and solve for the coordinates of the sound source. ; A three-dimensional virtual boundary is defined for each battery module, based on the calculated sound source coordinates. Located within the virtual boundary of its associated cell or module, thus: If the calculated sound source location is clearly outside the module boundary, it is determined to be an external impact or vibration and is immediately eliminated; Set a minimum time difference threshold; if the electromagnetic interference (EMI) signal arrives at each sensor within a certain time difference... If the time difference is less than the minimum time difference threshold, it is judged as EMI and rejected.

6. The lithium battery management method for emergency power supply guarantee during aircraft evacuation according to claim 1, characterized in that, The specific content of A4 includes: Add a unified timestamp to all data; A real-world internal battery event is the starting point of a causal chain, and its logic is as follows: Timing: A high-energy event that matches the characteristics and is located inside the battery, captured by the acoustic emission system, is recorded as an event. ; + Timing: Due to an internal short circuit causing a change in the internal resistance of the corresponding cell, the voltage monitoring process should be able to detect an abnormal sharp drop in the cell voltage, which is recorded as an event. ; + Timing: Due to ohmic heat generation at the internal short circuit point, the temperature sensor should be able to detect an abnormally significant increase in the rate of temperature rise in the cell or nearby area, which is recorded as an event. ; Create a time window within which events occur sequentially. , , This confirms the event as a genuine malfunction and triggers the highest level of alert.

7. The lithium battery management method for emergency power supply during aircraft evacuation according to claim 1, characterized in that, Step two specifically includes: Thresholds are divided into normal maintenance mode thresholds and emergency release mode thresholds; Emergency trigger signals from the aircraft's core avionics system are received directly via a dedicated hardwired signal; once this signal is true, the BMS master controller completes the mode switch. Immediately release the protection command based on the normal maintenance threshold; Load the emergency release mode threshold library; The mode switching command is sent synchronously to all corresponding parts.

8. The lithium battery management method for emergency power supply guarantee during aircraft evacuation according to claim 1, characterized in that, Step three specifically includes: An active balancing circuit is constructed using a flying capacitor or a multi-winding transformer. A simplified thermal model is built for each battery cell, with the cell's real-time current as the input. and current temperature The output is a temperature rise prediction within a preset future time period. ; When balancing is required, the BMS master controller simulates and calculates all possible balancing paths. For each path, the model predicts the module's maximum temperature during the equalization process due to energy transfer efficiency losses and equalization current flowing through internal resistance. Maximum temperature difference of the module ; And select one that makes the objective function The minimized equilibrium path, where and These are the weighting coefficients.

9. The lithium battery management method for emergency power supply during aircraft evacuation according to claim 1, characterized in that, Step four specifically includes: The gas sensor specifically detects carbon monoxide and hydrogen; the integrated micro-differential pressure sensor monitors minute changes in the internal pressure of the module. Construct an early warning model based on a long short-term memory network; the model inputs include: voltage, temperature, rate of temperature change, rate of voltage change, acoustic emission eigenvalues, rate of change of carbon monoxide concentration, rate of change of hydrogen concentration, and rate of change of pressure. The model's decision layer outputs the probability of thermal runaway risk.

10. The lithium battery management method for emergency power supply guarantee during aircraft evacuation according to claim 1, characterized in that, Also includes: Step 5: During emergency discharge, continuously record the key operating parameters of the battery system to an independently powered non-volatile memory; After the event ends, the system automatically analyzes the recorded data and generates a health status report that includes the total energy released, cell consistency performance, and maintenance recommendations.

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