10301 results about "Thermal runaway" patented technology

The invention discloses a battery pack thermal runaway risk identification and early warning system and method, and relates to the related field of battery risk early warning, and the method comprises the steps: collecting an operation parameter data set of a battery pack in real time through a multi-mode sensor network, carrying out the hybrid anomaly detection of the battery pack, obtaining a real-time anomaly detection result, and carrying out the thermal state prediction of the battery pack, and constructing a thermal state evolution trend chart to perform thermal runaway risk assessment, determining a thermal runaway risk level, triggering a multi-level early warning signal according to the thermal runaway risk level, and generating a thermal runaway suppression strategy. The technical problems that in existing battery thermal runaway early warning, due to monitoring limitation of a single sensor, the false alarm rate is high, response lags behind, and battery aging cannot be dynamically adapted are solved, and the technical effects that the thermal runaway early symptom detection rate is increased through multi-mode sensing, and the early warning time is shortened are achieved.

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention relates to the technical field of batteries, and discloses a battery thermal runaway early warning and cooling system which comprises a multi-parameter monitoring module, a data processing and early warning model and a cooling system control module. Wherein the multi-parameter monitoring module is used for acquiring the internal resistance value of each battery monomer in real time in a battery charging process, calculating the internal resistance range, the internal resistance standard deviation and the variable coefficient according to the internal resistance value at a sampling time point, monitoring the working environment gas of the battery through a mass spectrometer, and acquiring the real-time mass spectrum monitoring data of the gas in real time; release characteristics, including hydrogen, hydrocarbon compounds and the like, of escaped gas are quantitatively analyzed on the basis of an equivalent characteristic spectrum method. According to the battery thermal runaway early warning and cooling system, various key parameters in the battery charging process are comprehensively monitored, early warning of the battery thermal runaway risk is achieved, timely intervention is conducted in combination with the cooling system, the thermal runaway risk is reduced, and vehicle and personal safety is guaranteed.

The invention relates to the technical field of battery management, and discloses a multi-dimensional parameter evaluation system and method based on cell thermal runaway risk detection, a thermal-electric coupling data set is constructed by collecting multi-dimensional parameter data of a cell in different charging and discharging states, and electrochemical impedance spectroscopy data in different aging states are obtained; constructing a refined multi-dimensional electrochemical characteristic space; training a cell health degree evaluation model based on the feature space, extracting a cell health degree index, and establishing a mapping relationship between the cell health degree index and a thermal runaway risk level; in combination with the real-time working condition parameters of the battery cell, a working condition self-adaptive battery cell health degree index is obtained by dynamically adjusting the weight coefficient of each feature in the health degree evaluation model, the thermal runaway risk level is predicted according to the working condition self-adaptive battery cell health degree index, and an early warning mechanism is triggered; according to the invention, accurate, real-time and dynamic evaluation of the thermal runaway risk of the battery cell can be realized, the accuracy and reliability of early warning of the thermal runaway of the battery cell are remarkably improved, and powerful technical support is provided for safety management of the battery.

The invention relates to a lithium battery energy storage system fire-fighting ventilation and explosion venting safety assessment method based on multi-dimensional simulation. The method comprises the following steps: constructing an energy storage system digital twinborn model fusing structure parameters, material attributes and environmental parameters; generating a multi-mode failure scene set covering multiple temperature domains and aging states through mode recognition; simulating and quantifying dynamic interaction of a temperature field, a flow field and a stress field in the thermal runaway evolution process based on thermal-fluid-solid multi-physics field coupling; constructing a space-time associated dynamic safety evaluation matrix, and combining fuzzy comprehensive evaluation and Monte Carlo sampling to generate risk quantitative indexes; and iteratively correcting parameters of the fire-fighting ventilation and explosion venting system through a multi-objective optimization algorithm to form a graded safety assessment conclusion. According to the method, the technical bottlenecks of environmental parameter splitting and single failure scene in a traditional method are broken through, the thermal runaway suppression efficiency is improved, the combustible gas concentration control error is reduced, and collaborative optimization of explosion venting pressure fluctuation suppression and ventilation response is realized through a closed-loop evaluation mechanism.

The invention discloses a storage battery abnormity early warning system based on an intelligent battery gateway, and relates to the technical field of battery monitoring. The system comprises a sensor module used for collecting voltage, internal resistance, temperature, thermal runaway state and floating charge voltage parameters of a single battery in real time; the intelligent battery gateway comprises a data processing module and an early warning module, and the data processing module carries out fusion analysis on sensor data through an intelligent algorithm, calculates the battery remaining capacity SOC and the state of health SOH and executes an intelligent equalization strategy; and the early warning module generates a multi-stage early warning signal according to the parameter threshold and the anomaly detection model. According to the invention, through a multi-dimensional fusion sensing technology and a dual-drive intelligent algorithm, the aging sign of the internal microstructure of the battery is accurately captured, and high-precision estimation of the residual electric quantity and the health state of the storage battery is realized; and in the aspect of early warning performance, a multi-dimensional linkage detection model and an intelligent early warning mechanism are established, so that the accuracy and timeliness of fault early warning are effectively improved, and the early warning time is greatly advanced.

The invention provides an energy storage battery health state evaluation and thermal runaway risk prediction method and system, and belongs to the technical field of energy storage batteries. The method comprises the following steps: establishing an electrochemical fusion model by fusing an electrochemical model, a thermal model and an aging model; training the electrochemical fusion model, and inputting physical parameter data of the energy storage battery into the trained electrochemical fusion model to obtain key performance parameter data of the energy storage battery; obtaining an energy storage battery health state evaluation result based on the key performance parameter data of the energy storage battery; and comparing the key performance parameter data of the energy storage battery with data detected by an actual sensor to obtain a thermal runaway risk prediction result. Accurate evaluation and fault prediction of the energy storage battery health state are achieved, the precision and safety of battery management in an energy storage system can be remarkably improved, and the method is suitable for a large-scale energy storage system.

The present application relates to the technical field of batteries, and discloses a thermal runaway risk prediction method and apparatus, a device, and a storage medium. The method comprises: processing, by at least two neural network layers in a target prediction model, thermal runaway risk parameters layer by layer, wherein the target prediction model is obtained by pre-training on the basis of state vectors of a plurality of time steps, so that the memory capability of the model for past state sequences can be enhanced, and thus the model can better learn the dynamic characteristics of an energy storage battery system, and captures a complex temporal association relationship among multiple variables, thereby improving the accuracy of thermal runaway risk prediction and reducing the safety risk of the energy storage battery system.

The invention discloses an energy storage power station fire safety monitoring system based on a big data Internet of Things, and relates to the technical field of fire safety monitoring. A multi-dimensional sensor network is adopted to monitor parameters in real time, a multi-dimensional parameter model under normal working conditions is established based on a multivariable Gaussian distribution model, and the real-time monitoring of the parameters is realized. In the early stage of thermal runaway, abnormity is recognized through the Mahalanobis distance, an early warning signal can be triggered immediately when the abnormity occurs, the defects that in a traditional method, the monitoring means is single, and response lags behind are overcome, fire early warning is more accurate and timely, and the concentration of harmful gas is monitored in real time in combination with a gas detection module; sensor data and gas concentration data are fused through Bayesian inference, the fire risk is evaluated more comprehensively, the comprehensiveness of fire risk evaluation is remarkably improved, a fire area is positioned through a fire source positioning algorithm, and the fire extinguishing range is adjusted in real time according to the fire risk level and the dynamic change of fire source diffusion.

The invention belongs to the technical field of battery monitoring, and discloses a lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring. The method comprises the following steps: collecting multi-source battery parameters in real time, and marking the multi-source battery parameters as real-time multi-source battery parameters; the real-time multi-source battery parameters are processed, and battery characteristic parameters are extracted; performing fusion analysis on the real-time multi-source battery parameters and the extracted battery characteristic parameters, and predicting the thermal runaway probability of the lithium battery; judging whether to generate an evaluation instruction according to the thermal runaway probability; if the evaluation instruction is generated, evaluating the thermal runaway type of the lithium battery and starting active prevention and control measures; according to the invention, high-precision and timely monitoring and early warning of lithium battery thermal runaway accidents are realized, the thermal runaway risk is reduced, and the safety and reliability of lithium battery operation are effectively improved, so that the life safety of new energy automobile users is protected.

The invention discloses a battery cluster level fire-fighting linkage control method and system based on a BMS, and the method comprises the steps: carrying out the thermal runaway prediction through a multi-source feature fusion network according to the real-time voltage, temperature and gas concentration data of a battery cluster, and outputting a thermal runaway prediction level; on the basis of the thermal runaway prediction level, a dynamic priority mapping algorithm is utilized, and a control instruction set containing execution priority and spatial positioning is output according to the battery cluster topological structure; according to the control instruction set, a dual-channel redundant signal verification mechanism is adopted to execute a fire-fighting action, and a real-time action feedback signal is output; and on the basis of the real-time action feedback signal and the residual state data of the battery cluster, updating the parameter weight of the multi-source feature fusion network through a reinforcement learning model so as to be used for fire-fighting linkage control in the next period, and forming a closed-loop control link. According to the embodiment of the invention, battery cluster-level fire-fighting linkage control can be realized, so that the safety protection capability of a battery system is improved.

The invention discloses a lithium battery health state assessment method and system, and particularly relates to the technical field of battery health state assessment. The method comprises the following steps: performing time sequence alignment and structured preprocessing on multi-source operation data of a target lithium battery in a plurality of historical work cycles to construct a structured data set; constructing a spatial-temporal characteristic residual error map based on residual error mapping analysis, and extracting a spatial heterogeneity index; in combination with a spatial heterogeneity index, generating regional degradation feature mapping; through high-dimensional feature embedding and evolution path clustering, a heterogeneous aging mode is identified, and a classification result is generated; evaluating the health state grade of the target lithium battery according to the regional degradation characteristic mapping and heterogeneous aging mode classification result; whether the battery has a local potential thermal runaway risk or not is judged based on the evaluation result, and a corresponding risk early warning signal and a safety disposal suggestion are generated, so that the nonlinear influence of the lithium battery aging heterogeneity can be accurately identified, and the health state evaluation precision and the safety risk early warning capability are effectively improved.

The invention discloses an electric ship battery pack temperature control method, and relates to the technical field of ship battery pack temperature control, and the method comprises the following steps: arranging a plurality of temperature sensors in a battery pack, monitoring the temperature data of each single battery in real time, and transmitting the temperature information of each single battery to a central processing unit in real time; through multi-sensor arrangement, heat conduction model calculation and intelligent cooling system scheduling, accurate temperature monitoring and dynamic adjustment of the electric ship battery pack are realized, real-time abnormal detection is ensured, and thermal runaway is prevented. The intelligent cooling system accurately adjusts cooling force according to temperature changes, and safety accidents are avoided. Meanwhile, a cooling strategy is optimized, energy consumption is reduced, system efficiency is improved, battery life is prolonged, sailing mileage is increased, maintenance cost is reduced, and long-term stable and efficient operation of the battery pack is ensured.

The invention discloses a battery thermal runaway detection system based on an early warning function, and relates to the technical field of battery thermal runaway, and the system comprises a supervision center which is used for carrying out the real-time supervision of the data information of a plurality of batteries, carrying out the early warning of the time data of the battery thermal runaway, and facilitating the timely issuing of warning information and notification announcements, and a monitoring device which is used for monitoring the data information of a plurality of batteries. The monitoring device is used for collecting thermal runaway parameters of each detection node of a plurality of batteries, the thermal runaway parameters comprise battery voltage, temperature, current and pressure, and the protection device is used for carrying out delayed protection operation on the monitoring device. The battery thermal runaway detection system based on the early warning function establishes a thermal runaway early warning model by fusing multi-parameter data of heat, force, electricity and the like and historical data of a thermal runaway phenomenon and by utilizing various learning algorithms, monitors battery parameters and sets simple judgment standards and threshold values such as a non-working state current threshold value and the like, so that the battery thermal runaway early warning model is established. Early detection of battery thermal runaway is realized, and monitoring and early warning effects are achieved.

The invention discloses a lithium ion battery thermal runaway multi-dimensional characteristic parameter early warning method, which comprises the following steps: S1, collecting internal multi-dimensional characteristic parameters of a battery pack in real time, including an expansion force signal, a voltage signal, a characteristic gas concentration signal and a temperature distribution signal; s2, constructing a multi-parameter collaborative analysis model, performing dynamic fusion analysis on the expansive force signal, the voltage signal, the characteristic gas concentration signal and the temperature distribution signal, and outputting a thermal runaway risk value through a weighted decision algorithm; s3, comparing the thermal runaway risk value with preset different early warning thresholds in an extremely early stage, determining a thermal runaway risk level, and outputting a multi-signal collaborative early warning instruction according to the thermal runaway risk level; and S4, linkage triggering is carried out on the battery pack to carry out cooling, smoke exhausting, fire extinguishing and explosion suppression prevention and control treatment. According to the invention, the problems of manual early warning disposal, missing of an optimal prevention and control window and high false alarm rate of single-parameter early warning are solved.

The invention discloses a lithium ion battery thermal runaway early warning method, and belongs to the technical field of battery safety monitoring, and the method comprises the steps: synchronously obtaining a low-frequency sound wave signal, a temperature signal, a voltage signal and a stress-strain signal of a lithium ion battery; noise reduction processing is carried out on the low-frequency sound wave signals, time-frequency feature extraction is carried out on the low-frequency sound wave signals after noise reduction processing, and feature frequency band energy corresponding to a thermal runaway early event is obtained; a thermal runaway sensitivity coefficient is calculated based on the characteristic frequency band energy, and when the thermal runaway sensitivity coefficient is larger than a first coefficient threshold value, thermal runaway early warning is triggered; and on the basis of the thermal runaway sensitivity coefficient, the temperature signal, the voltage signal and the stress-strain signal, constructing a feature vector, and inputting the feature vector into a trained long-short-term memory network and attention mechanism hybrid model for classification to obtain a thermal runaway early warning level. The method can solve the problems of installation difficulty, detection lag and high false alarm rate in the prior art.

The present invention relates to the technical field of battery separators and provides a coated modified composite separator, a preparation method thereof and a coating slurry for preparing the composite separator. The coated modified composite separator includes a base membrane and a coating, wherein the coating is coated on either any one side or both sides of the base membrane, and the coating comprises at least two of the following: b2.1 high temperature resistant polymer microspheres, b2.2 high temperature resistant polymer nanofibers, and b2.3 inorganic particles. The coating in the modified composite separator of the present invention notably enhances the thermal dimensional stability of the base membrane, decreases the areal density of the composite separator, boosts the battery's energy density, lowers the probability of occurrence of thermal runaway, and improves battery safety.

The invention provides a concrete prefabricated energy storage cabin which comprises a cabin body and a cabin door which are made of a concrete composite material, so that the energy storage cabin has high compressive strength and high fire resistance rating, and a plurality of energy storage cabins can also be transversely arranged without spacing or stacked in the height direction, so that the occupied area is effectively reduced; an energy storage box provided with a basin-shaped box body and a cover plate is arranged in the cabin body, the cover plate covers the basin-shaped box body to form a closed energy storage cavity for accommodating a battery module, and the basin-shaped box body and the cover plate are also made of a concrete composite material, so that high temperature generated by thermal runaway of a battery in a single energy storage box can be effectively limited in the energy storage box; as the oxygen content in the energy storage box is relatively low, the flame can be automatically extinguished after the battery with relatively light thermal runaway degree is on fire; and for a battery fire with a heavy thermal runaway degree, a long time can be provided for fire fighting and extinguishment, and flames can be extinguished in time in the energy storage box.

The invention provides a new energy vehicle aggregation prevention and control system based on clustering identification and regional risk modeling, and aims to solve the potential safety hazards such as thermal runaway caused by high-density vehicle aggregation. The system comprises a state acquisition module, a clustering identification module, a regional risk modeling module, a risk grade determination module, a response control scheduling module and a thermal risk prediction module. The system collects running state data such as vehicle position, battery temperature and charge state, uses a density-based spatial clustering algorithm to identify clusters, calculates risk score values of the clusters, and implements current limiting and path guiding control according to risk levels. Meanwhile, the risk change trend of a future area is predicted through the trained long-short-term memory neural network model, and early warning and response are achieved in advance. The system is suitable for safety management scenes of new energy vehicles in high-density areas such as charging stations and underground garages.

The invention relates to the technical field of lithium battery safety monitoring, and discloses a lithium battery pack thermal runaway early warning system based on multi-mode sensing. The system comprises multi-source sensing data acquisition, thermal field feature tensor construction, thermal field reconstruction and thermal coupling association network generation. The multi-source sensing data acquisition module acquires multi-dimensional heterogeneous data from a temperature sensor, a voltage and current monitoring unit, a gas component detector and an acoustic emission sensor, and generates a standardized data bin through timestamp alignment and missing value compensation; the thermal field feature tensor construction module extracts features such as temperature gradient and electrochemical response from the data bin in a multi-scale manner, and constructs a tensor in combination with time continuity; the thermal field reconstruction module generates association diagrams according to the feature space-time distribution and fuses the association diagrams into a lithium battery pack three-dimensional thermal field reconstruction map; and the thermal coupling association network generation module extracts a feature vector cluster, calculates an entropy weight value, and generates a network according to a high-entropy node space adjacency relationship. According to the system, multi-dimensional data fusion is realized, and the thermal runaway evolution law can be comprehensively described.

The invention provides a thermal runaway prevention and heat dissipation optimization method and system based on a capacitor module, and relates to the technical field of capacitor module thermal management, and the method comprises the steps: extracting the multi-source operation data features of a single capacitor through an improved layered visual attention network, carrying out the data complementation through combining a bidirectional probability diffusion model, and obtaining a multi-source operation data feature of the single capacitor; and discovering a causal relationship between network identification data by using a causal structure, generating a performance analysis report, constructing a spatio-temporal dynamic graph neural network to predict temperature field distribution, performing fault diagnosis and uncertainty quantification in combination with a multi-task learning framework, further constructing a layered early warning mechanism, and performing early warning based on a layered reinforcement learning framework. And a dynamic prediction map and a performance analysis report are combined, a heat dissipation strategy is optimized, cooperative control of the heat dissipation units is realized, an optimal heat dissipation scheme is finally obtained, thermal runaway is effectively prevented, and the safety and reliability of the capacitor module are improved.

The invention relates to the technical field of photovoltaic power generation, and discloses a BAPV-based roof photovoltaic intelligent monitoring system and a dynamic tuning method and device. The method comprises the following steps: scanning a preset BAPV assembly through three-dimensional laser, constructing a surface point cloud model, and establishing a three-dimensional coordinate system comprising BAPV assembly installation base points; acquiring a real-time environment parameter set corresponding to the BAPV component; calculating an optimal working angle of the BAPV component according to the real-time environment parameter set based on an improved particle swarm algorithm; simulating an all-weather shadow movement track according to the optimal working angle based on a Monte Carlo method, and generating a component shielding influence weight matrix; establishing a heat balance equation according to the component shielding influence weight matrix so as to calculate a spacing optimization parameter of the BAPV component; a control instruction is generated according to the spacing optimization parameters, and dynamic tuning of the BAPV assembly is completed according to the control instruction; the control instruction comprises a support rotation angle compensation amount, an inverter parameter adjustment amount and an abnormal component isolation instruction. And the pain points of modeling errors, static optimization limitation, thermal runaway risks and the like are solved.

The present application relates to the field of electric power, and provides a knowledge graph-based energy storage power station fault identification method and apparatus. The knowledge graph-based energy storage power station fault identification method comprises: acquiring state data of an energy storage power station; inputting the state data into a pre-constructed energy storage power station fault identification model, and predicting a fault condition and an evolution path of the energy storage power station, wherein the energy storage power station fault identification model is constructed on the basis of a thermal runaway knowledge graph, and the thermal runaway knowledge graph is used for representing an association relationship between the state data of the energy storage power station and a thermal runaway process; and on the basis of the fault condition and the evolution path, determining a safety risk level of the energy storage power station. The present application can comprehensively and accurately evaluate the operation condition of the energy storage power station, and achieve long-time fault early-warning.

The invention relates to the technical field of new energy battery testing, and discloses a new energy battery safety testing system and method, and the system comprises a multi-physical field synchronous collection and analysis module which is used for synchronously collecting electric signals, temperature field distribution and stress-strain data in a battery testing process, and carrying out the multi-field coupling analysis; a self-adaptive test parameter regulation and control module; the thermal runaway risk prediction module is used for analyzing and predicting the risk probability and residual safety time of thermal runaway of the battery based on multi-parameter fusion; the microstructure-macroscopic performance correlation analysis module is used for acquiring microstructure information of the battery through nondestructive testing and establishing a correlation model with macroscopic safety performance, and the new energy battery safety test system realizes collaborative analysis of multiple parameters of electricity, heat and force through the multi-physics field synchronous acquisition and analysis module; the safety state of the battery can be reflected more comprehensively, and the defect that a traditional testing method is single in dimension is overcome.

The invention discloses a battery thermal runaway risk intelligent management method and system, and the method comprises the steps: matching corresponding cooling liquid cooperative ultrasonic cooling words according to different risk levels, i.e., absorbing heat generated by conventional operation of a battery through employing the higher specific heat capacity of cooling liquid when the risk is low and the working state of the battery is normal; and the heat control of the battery can be realized. In case of risks such as medium and the like, when a multi-dimensional sensing signal of the battery is slightly abnormal, circulation is started, so that the cooling liquid flows to take away heat generated by the battery, and the cooling liquid is excited to be slightly evaporated and vibrated to enhance heat exchange through relatively low-intensity ultrasonic waves so as to uniformize the temperature in the battery module. When the risk is high, the cooling liquid completely immerses the top of the battery to isolate oxygen, the flowing speed of the cooling liquid is increased to rapidly take away heat, high-strength and high-frequency ultrasonic waves are emitted, rapid evaporation and heat absorption of the cooling liquid are enhanced, the temperature is rapidly reduced, and thermal runaway of the battery is avoided.

The invention discloses a battery thermal management method, device and system and a vehicle, and relates to the technical field of battery management. A specific embodiment of the method comprises the following steps: acquiring battery data of a battery, acquiring a maximum battery cell temperature from the battery data, and extracting battery characteristics; determining the thermal runaway probability of the battery according to the battery data and the battery characteristics; selecting a target early warning strategy matched with the maximum cell temperature and the thermal runaway probability from a preset multi-level early warning strategy and a preset hidden state early warning strategy; according to the environment information of the environment where the battery is located and the battery characteristics, rule parameters in the target early warning strategy are adjusted, and regulation and heat dissipation are conducted on the battery through the adjusted target early warning strategy. According to the embodiment, the rule parameters of the early warning strategy are adaptively adjusted according to the actual state of the battery, a fixed threshold value judgment mode is replaced, advanced recognition of potential hazards and dynamic optimization of the early warning strategy are achieved, and therefore early warning accuracy and flexibility are greatly improved.

The invention discloses a new energy automobile lithium battery thermal runaway monitoring system and method. The system comprises a vehicle-mounted lithium battery, a multi-source data acquisition module, a multi-source data fusion module, a hidden Markov model monitoring module, a state identification module, a data analysis processing module, an early warning module, an emergency processing module, a wireless communication module, a cloud platform and a battery management system. The multi-source data acquisition module is responsible for acquiring multi-dimensional data such as voltage, temperature and internal resistance of a lithium battery, and the data fusion module processes the data and then transmits the data to the hidden Markov model monitoring module for state monitoring and prediction. According to the system, a high-order hidden Markov model and a hidden semi-Markov model are adopted, state retention time modeling and a self-adaptive online learning mechanism are combined, and conversion of a battery from a normal state to an abnormal state and a thermal runaway state can be accurately recognized. The system remarkably improves the safety and stability of the lithium battery of the new energy automobile, and is suitable for power battery management of the new energy automobile.

The invention discloses a ternary internet-of-things battery management system, particularly relates to the technical field of lithium battery management, realizes lithium battery thermal safety closed-loop management through multi-sensor fusion, and comprises the following steps: deploying a digital sensor array to obtain time-space associated data; constructing a spatio-temporal feature vector including fusion of a local temperature gradient, a neighborhood temperature gradient and a stress coupling ratio; a density clustering algorithm is adopted to dynamically divide spontaneous heating, radiant heat and stress coupling clusters, and cluster boundaries are updated in real time; calculating a dual thermal coupling coefficient based on a clustering result, and eliminating the interference of temperature reading through a dual temperature compensation formula; a three-level alarm mechanism of a radiant heat proportion, a stress coupling degree and a compensation error is established, a side gateway is linked to realize parameter adaptive optimization, and the thermal runaway early warning precision and the structural failure detection rate are improved; strain monitoring resource optimization is realized through physically-driven key point screening and dynamic graph neural network prediction.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.

The invention relates to the technical field of electric forklifts and energy storage, and discloses an intelligent battery management system and prediction method based on optical fiber sensing, the system comprises a distributed optical fiber sensing network, a data acquisition and processing module, an intelligent analysis decision module, a battery management control module and a cloud platform interaction module; the method comprises the following steps: collecting a battery signal through a snakelike-spiral optical fiber network, reconstructing a temperature / strain field through noise reduction and filtering, extracting multi-modal features, inputting the multi-modal features into an LSTM model to predict thermal runaway, generating a risk level in combination with an improved equation, dynamically adjusting charging, discharging and heat dissipation strategies, and realizing three-dimensional visual monitoring through a cloud platform. According to the invention, high-precision distributed monitoring of the temperature / strain field of the battery pack is realized through the BOTDR optical fiber sensing network, the LSTM prediction model is constructed by fusing multi-physical field characteristics, and thermal runaway is warned in advance. The improved algorithm reduces the false alarm rate, charging and discharging parameters are dynamically adjusted to prolong the service life of the battery, and a three-dimensional visual platform improves the operation and maintenance efficiency.