ML for Lithium-Ion Battery Thermal Runaway Prevention in EVs

Early warning system for fire prevention using visual and thermal sensing. The system combines visual and thermal images to detect temperature anomalies and identify the object causing the anomaly. This provides object-aware temperature monitoring to prevent false alarms and accurately predict fire risks. Computer vision is applied to visual images to classify objects, while thermal images detect temperature changes. By fusing the data, sudden temperature spikes in specific object features like batteries can be linked to their location and identified as fire hazards.

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Intelligent vehicle system that predicts and responds to thermal runaway events in vehicle batteries when parked in enclosed spaces. The system detects battery cell overheating and determines if the vehicle is parked in an enclosed area using sensors and vehicle data. If so, it alerts nearby people and first responders about the thermal event and provides instructions to evacuate the area. This mitigates risks from battery fires when parked indoors. The system also disconnects the battery to prevent spreading.

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Detecting anomalies in the behavior of electrical energy storage devices like vehicle batteries without relying on physical sensors inside the battery. The method involves monitoring operating variables of the battery, extracting features from the operating profiles, and using a variational autoencoder to compare the features against a distribution. A deviation from the distribution indicates an anomaly. This allows detecting battery issues without needing to directly measure internal battery aging.

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Determining abnormalities in time-series waveform data using machine learning. The method involves learning abnormality using an autoencoder and persistent homology conversion. The autoencoder learns normal waveforms. Persistent homology calculates changes in connected components based on threshold changes. A learner with inputs from the autoencoder and persistent homology determines abnormality. This improves accuracy compared to just using the autoencoder by capturing more waveform features.

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Thermal runaway in lithium-ion batteries generates toxic and flammable gases such as CO, C2H2, C2H4, presenting severe safety risks. Early detection of these is essential for battery monitoring. In this study, the gas sensing performances pristine Pt-doped indium selenide (InSe) monolayers toward C2H4 were investigated using first-principles density functional theory (DFT). The adsorption energies, charge transfer, electronic structure changes systematically analyzed to evaluate sensitivity. Results indicate that InSe exhibits weak interaction with all target gases. However, Pt doping significantly enhances increased energies substantial transfer. dopant also modulates InSe, which favorable signal transduction. These findings demonstrate possess excellent selectivity sensitivity critical warning from thermal runaway, offering theoretical guidance design high-performance 2D material-based sensors.

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Detecting abnormal temperatures in real-time with high accuracy, even when there is a change in ambient temperature. The method involves calculating temperature features, synthesizing them with past normal/abnormal indicators, training a model on the synthesized data, and using the model to determine if new temperature data is normal or abnormal. This allows accurate detection of abnormal temperatures even when ambient temperature changes, as the synthesis captures the difference between normal and abnormal temperature trends.

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Predicting thermal propagation between battery cells to prevent thermal runaway events. A thermal model is used to determine the likelihood of thermal propagation between cells based on their temperatures. The model is received by the battery management system and applied to the temperatures of specific cells to calculate the risk of thermal propagation between them. This helps prevent chain reactions that can lead to runaway conditions.

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Predicting thermal runaway events in battery packs to provide earlier warning and enable mitigation before catastrophic failure. The prediction is based on a thermal runaway model that takes attributes of the battery cells as input. The model simulates structural deformation, electrochemical behavior, and module-level interactions to predict if a cell will undergo thermal runaway. By feeding real-time cell data into the model, it can alert when conditions are trending towards runaway, giving time to intervene and potentially prevent the event.

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Abstract Thermal runaway in lithium-ion batteries is a critical safety concern for the battery industry due to its potential cause uncontrolled temperature rises and subsequent fires that can engulf pack surroundings. Modeling simulation offer cost-effective tools designing strategies mitigate thermal runaway. Accurately simulating chemical kinetics of runaway, commonly represented by systems Arrhenius-based Ordinary Differential Equations (ODEs), requires fitting kinetic parameters experimental calorimetry data, such as Accelerating Rate Calorimetry (ARC) measurements. Particle Swarm Optimization (PSO) offers promising approach directly data. Yet, created multiple Arrhenius ODEs, computational cost using brute-force searches entire parameter space simultaneously become prohibitive. This work introduces divide-and-conquer based on PSO fit N-equation ODE models ARC The proposed method achieves more accurate compared while maintaining low costs. analyzed two distinct datasets, resulting are further validated through simulations 3D oven tests, showing excellent agreement with data align... Read More

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Thermal runaway remains a critical safety challenge for lithium-ion batteries (LiBs) used in electric vehicles (EVs), with varying characteristics across different chemistries. This study presents comparative analysis of thermal predictions five widely LiB chemistries: Nickel Manganese Cobalt (NMC), Aluminum (NCA), Lithium Iron Phosphate (LFP), Oxide (LMO), and Titanate (LTO). A hybrid methodology combining controlled experimental abuse tests advanced physics-based machine learning models was employed to predict onset temperatures propagation behavior. Results reveal significant differences stability prediction accuracy among chemistries, LFP LTO exhibiting higher more reliable model predictions, whereas NMC NCA showed earlier rapid temperature escalation. These findings have direct implications battery management system (BMS) design protocols EVs, emphasizing chemistry-specific thresholds response strategies.

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Ensuring the safety of lithium-ion power batteries is primary prerequisite for developing electric vehicles and energy storage systems. The conventional method relies on temperature parameters only qualitatively assesses state (SOS), which reduces warning time battery management system (BMS). Here we present a thermal runaway based SOS. Specifically, analyze strain evolution trend under different abuse conditions propose trigger point runaway. Furthermore, multidimensional such as rise, median voltage, capacity, power, are used to quantify SOS parameter, with its value ranging from 0% 100%. Experimental results demonstrate that presented approach can warn around 5 h in advance.

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Automatically generating context-aware machine learning models for anomaly detection using feedback from model outputs. The technique involves generating multiple machine learning models using different field combinations from the input data. This provides context-awareness as the models analyze the data from multiple perspectives. Feedback from model outputs over time is analyzed to improve the models. This closed-loop process continuously enhances model quality and reduces false positives.

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Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical these techniques can be challenging due interplay cantilever physics with actual transient kinetics interest resulting signal. Previously, quantitative trEFM used empirical calibration or feed-forward neural networks trained on simulated data extract interest. Both approaches are limited by interpreting signal as a single exponential function, which serves an approximation but does not adequately reflect many realistic systems. Here, we present multibranched, multioutput convolutional network (CNN) that uses addition parameters input. The CNN accurately extracts describing both single-exponential and biexponential functions more reconstructs real experimental presence noise. This work demonstrates application physics-informed machine learning complex processing tasks, enabling efficient accurate ana... Read More

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Early detection of abnormally deteriorated cells in battery modules to prevent safety issues like thermal runaway or ignition. The method involves monitoring capacity deviations of multiple cells over time. It calculates statistical values representing the capacity variations at two different time points. By comparing these values, it can detect cells that have significantly deteriorated compared to the others, indicating an abnormality.

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The thermal runaway of lithium-ion batteries is a critical factor influencing their safety. Investigating the characteristics essential for battery safety design. In this study, 18650 under different SOCs were systematically analyzed by experiment and simulation. It was found that at high SOC (100%), highly lithium state accelerated lattice oxygen release, promoted formation LiNiO intensified electrolytic liquid oxygenation combustion, while low (20%), reduction environment dominated, metal Ni residual graphite significantly enriched. Gas analysis shows CO2 H2 account more than 80%, proportion regulated SOC. Temperature pressure monitoring showed increase in increased peak temperature (100% up to 508.4 C) (0.531 MPa).The simulation results show when pack out control, ejection fire explosion wave are concentrated middle upper region (overpressure 0.8 MPa). This study reveals mechanism which affects path product gas generation regulating oxidation/reduction balance, lays theoretical foundation safe design quantitative evaluation runaway.

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Lithiumion batteries are the dominant battery type for emerging technologies in efforts to slow climate change. Accurate and quick estimations of state charge (SOC) internal cell temperature vital battery-management systems enable effective operation portable electronics electric vehicles. Therefore, a long short-term memory (LSTM) recurrent-neural network is proposed which completes estimation SOC average (Tavg) lithiumion under varying current loads. The trained evaluated using data compiled from newly developed extended single-particle model coupled with thermal dynamic model. Results promising, root mean square values typically 2% 1.2 K Tavg, while maintaining training testing times. In addition, we examined comparison single-feature versus multi-feature network, as well two different approaches partitioning.

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Abstract The widespread adoption of lithium batteries has raised concerns regarding safety due to the risk thermal runaway. mechanisms by which State Charge (SOC) and internal heat generation influence runaway in these remain unclear. In this study, we utilize a three-dimensional electrochemical-thermal coupled model investigate evolution with increasing temperature examining growth rate. Our findings indicate that: (1) both rate during initially increase before eventually decreasing; (2) an SOC leads reduction intercalation concentration at negative electrode, resulting decreased lower peak temperatures runaway; (3) significantly impacts critical time runaway, higher associated increased risk. These results provide theoretical support for optimizing battery preventing fires explosions.

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The paper presents the concept and modeling results of a multisensor system designed to prevent thermal runaway in lithium-ion batteries. This is especially true for LCO, NMC NCO integrates three types sensors: capacitive pressure sensor, gas sensor based on metal oxide semiconductor, platinum temperature sensor. Moreover, all sensors are located single chip, which ensures increased reliability safety, minimizing risks fire, explosion, or damage Three battery operating modes proposed: normal, hazardous, critical. In normal mode, concentration remain at safe levels, while hazardous they begin increase, indicating possible onset destructive reactions. critical reaches can lead damage, explosion. was modeled using COMSOL Multiphysics 6.1 package finite element method. approach helps improve safety batteries by solving problems monitoring their condition. scalability makes it suitable applications both portable electronics electric vehicles.

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<div class="section abstract"><div class="htmlview paragraph">Thermal runaway in battery cells presents a critical safety concern, emphasizing the need for thorough understanding of thermal behavior to enhance and performance. This study introduces newly developed AutoLion 3D model, which builds on earlier 1D framework offers significantly faster computational performance compared traditional CFD models. The model is validated through simulations heat-wait-search mode Accelerating Rate Calorimeter (ARC), accurately predicting by matching experimental temperature profiles from peer-reviewed studies. Once validated, employed investigate LFPO under controlled heating conditions, applying heat one or more surfaces at time while modeling transfer non-heated surfaces. primary objective understand how these localized patterns impact profiles, including average core temperatures surface each heated surface, examine evolution distributions over time. Additionally, explores effects varying rates using ARC assess different influence process. comprehensive approach aims provide valua... Read More

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<div class="section abstract"><div class="htmlview paragraph">Thermal runaway is a critical phenomenon in lithium batteries, characterized by self-sustaining process due to internal chemical reactions, that triggered once certain temperature reached within the cell. This event often caused overheating charge and discharge cycles can lead fires or explosions, posing significant safety threat. The aim of this study induce thermal on single cells different ways characterize validate simulation models present Altair SimLab.</div><div paragraph">The work was conducted several key phases. Initially, an experimental test performed calorimeter (EV ARC HWS test) collect data Molicel 21700 P45B cell during under adiabatic conditions. These were used for cell, allowing detailed comparison with results. Subsequently, operational conditions, overheated using heat pad powered at constant power (non-adiabatic conditions), monitoring through three thermocouples placed For scenario as well, replicating effect source surface.</div><div paragraph">This work, carrie... Read More

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<div class="section abstract"><div class="htmlview paragraph">This Paper will focus on simulating thermal runaway propagation within a battery cell and module. The model parameters are derived from accelerating rate calorimeter (ARC). simulation involves that converts the stored energy of materials into energy, thereby runaway. initiation is modelled through nail penetration event, represented by heat profile in region. resulting temperature rise this area triggers short model, leading to spread runaway.</div><div paragraph">For single-cell simulation, 1-equation used, focusing direct conversion cell. In contrast, module more complex scenario. Here, an initial near region activates which subsequently 4-equation abuse model. higher activation energies required initiate cascading effect, driving process throughout As increases further, internal intensifies, turn reactivates associated with compared those create effect accelerates process.</div><div paragraph">The results obtained both levels be validated against ARC data. A detailed discussion these ... Read More

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<div class="section abstract"><div class="htmlview paragraph">The surge in electric vehicle usage has expanded the number of charging stations, intensifying demands on their operation and maintenance. Public often exposed to harsh weather unpredictable human factors, frequently encounter malfunctions requiring prompt attention. Current methods primarily employ data-driven approaches or rely empirical expertise establish warning thresholds for fault prediction. While these are generally effective, artificially fixed they prediction limit adaptability fall short sensitivity special scenarios, timings, locations, types faults, as well overall intelligence. This paper presents a novel model equipment that utilizes adaptive dynamic enhance diagnostic accuracy reliability. By integrating quantifying Environmental Influence Factors (EF), Scenario (SF), Fault Severity (FF), Charging Equipment Status (CF) into cohesive predictive framework, our dynamically adjusts based comprehensive analysis factors. Using dataset 560,000 records from Hangzhou, employs batch offline reinforcement... Read More

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<div class="section abstract"><div class="htmlview paragraph">Interest in Battery-Driven Electric Vehicles (EVs) has significantly grown recent years due to the decline of traditional Internal Combustion Engines (ICEs). However, malfunctions Lithium-Ion Batteries (LIBs) can lead catastrophic results such as Thermal Runaway (TR), posing serious safety concerns their high energy release and emission flammable gases. Understanding this phenomenon is essential for reducing risks mitigating its effects. In study, a digital twin an Accelerated Rate Calorimeter (ARC) under Heat-Wait-and-Seek (HWS) procedure developed using Computational Fluid Dynamics (CFD) framework. The CFD model simulates heating cell during HWS procedure, pressure build-up within LIB, gas venting phenomena, exothermic processes LIB degradation internal components. validated against experimental NCA 18650 similar conditions, focusing on temperature domain pressure. effectively captures heat released by undergoing TR through convection radiation surrounding air while providing temporal spatial resolution compo... Read More

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Early warning system to detect thermal runaway in electric vehicle batteries using acoustic sensors. The system monitors sound waves emitted from battery cells to predict thermal runaway before it escalates. Acoustic sensors detect low frequency infrasound generated by gas bubbles forming during early stages of thermal runaway. An algorithm analyzes the acoustic data to predict thermal runaway. This allows earlier intervention to prevent escalation compared to temperature sensors.

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Estimating the internal temperature of a battery using impedance measurements at specific frequencies. The method involves applying a signal of a set frequency band to the battery, measuring the impedance, and using that impedance to estimate the internal temperature using a learning model. The set frequency band is determined based on a correlation between impedance and state of charge. By estimating internal temperature this way, it enables predicting battery fires before they occur.

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Multi-model fusion power battery thermal runaway early warning system that uses multiple warning models to accurately predict and warn of power battery thermal runaway failures. The system uses voltage, temperature, and heat transfer models to comprehensively monitor battery modules. It involves real-time acquisition of single cell voltages, historical voltage data, and internal temperature prediction using neural networks. The models consider voltage anomalies, physical heat generation, and cell-to-cell heat transfer. A decision model combines warnings for overall thermal runaway prediction. The multi-model fusion provides accurate early warnings and informed driver response to mitigate battery failure hazards.

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Lithium battery thermal runaway early warning method to improve accuracy and reduce calculation time compared to traditional methods. The method uses a deep learning model that integrates an offline electrothermal coupling model with an online model for surface temperature and voltage. A BiGRU neural network is trained using COA optimization to predict battery internal temperature. The method divides overcharge into three stages based on heat sources. This multi-stage prediction improves accuracy compared to single-stage direct prediction.

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Power battery temperature management method using a machine learning model to predict battery temperature and determine safe operating conditions. The method involves acquiring battery temperature and charge/discharge power data, extracting temperature correlation features, feeding them into a pre-trained LSTM model to predict future battery temperatures, and comparing the predictions to a threshold to determine temperature safety. This improves response speed and accuracy compared to conventional PID control.

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Battery thermal runaway early warning system using an electrochemical mechanism model to improve accuracy and reduce false alarms compared to traditional methods. The system predicts and warns of battery thermal runaway based on analyzing the battery's heat generation and iterating a model using real-time temperature, heating rate, and voltage data. It proposes a situation-based early warning scheme based on thermal trigger mechanisms rather than just monitoring temperature.

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An advanced warning system for battery thermal runaway using strain, temperature, and growth rate monitoring. The system uses a CNNLSTM neural network to detect when a battery is entering thermal runaway based on filtered strain, temperature, and growth rate data. It also defines a thermal runaway state using envelopes of the growth rates to quantify the severity of the runaway. The system provides early warning of thermal runaway by accurately predicting when it will occur and quantifying the runaway state in real-time.

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Early warning system for energy storage battery systems to prevent thermal runaway and improve safety. It uses real-time battery monitoring and simulation to detect and mitigate thermal runaway risks. The system obtains parameters like cell voltage, temperature, and gas concentration. It simulates cell behavior based on these inputs. By combining parameters like smoke concentration and module temperature, it determines thermal runaway risk levels. The system then takes appropriate actions based on the level to prevent escalation and protect the battery system.

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Early warning system to detect thermal runaway in energy storage systems like battery packs in electric vehicles or grid storage applications. The system uses a neural network model trained on historical temperature data to predict future temperatures. If the predicted temperature deviates significantly from actual readings or the reconstruction error is high, it indicates potential thermal runaway. This provides a more reliable and proactive warning compared to just monitoring individual sensor readings.

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Battery cabinet thermal runaway early warning method based on internal temperature estimation to accurately predict and prevent battery thermal runaway in energy storage systems. The method involves estimating the internal temperature distribution of the battery using a thermodynamic model considering charge/discharge current, voltage, resistance. This internal temperature is used to calculate the thermal runaway risk index. A multi-parameter early warning model combines this index, ambient temp, battery current to determine if early warning is needed. It provides real-time monitoring and warning of thermal runaway risk without requiring additional hardware.

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Early warning system for predicting battery thermal runaway to enable proactive intervention and mitigation. It uses a multi-factor scoring approach based on electrical and temperature characteristics to predict battery thermal runaway. The scoring involves factors like voltage overshoot, rate of voltage drop, temperature rise rate, and critical temperature. If scores consistently fall below thresholds, it indicates imminent thermal runaway.

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Temperature control for new energy batteries using a spatiotemporal recurrent neural network (STRNN) to accurately predict and mitigate thermal management risks. The STRNN model is trained using a customized loss function that prioritizes accurate prediction of high-risk thermal states. This allows the model to better anticipate and prevent potential thermal runaway events. The STRNN takes into account factors like battery environment, operating conditions, and history to provide proactive temperature control for new energy batteries in complex and variable environments.

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Early warning system for lithium-ion batteries in vehicles to detect and alert against thermal runaway caused by collision impacts. The system uses a combination of sensors, neural networks, and machine learning techniques to predict and mitigate battery failure and temperature spikes after collisions. It collects multiple battery characteristic physical quantities like voltage, temperature, and current to explore the connection between input features and failure. A combined neural network model with a deep CNN and LSTM predicts battery failure and temperature after collision. It uses weighted moving average filtering to smooth data, normalization to improve convergence, and multi-level warning strategies. This allows early warning before thermal runaway to take protective measures.

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Predicting and preventing thermal runaway in lithium-ion batteries used in electric vehicles to improve safety. The method involves predicting battery thermal runaway risk using a model based on battery status data, and then taking appropriate actions to prevent further runaway if risks are detected. The model analyzes factors like voltage, current, temperature, and charge level to predict battery state and identify triggers like overheating or internal shorts. If runaway indicators exceed thresholds, measures like cooling, ventilation, or disconnecting the battery are taken to prevent full runaway.

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Battery thermal runaway prediction method to determine if a vehicle's battery is at risk of overheating and thermal runaway. The method involves a two-step process: (1) assessing if the vehicle meets conditions indicating thermal runaway risk, and (2) if so, using a trained machine learning model to predict the battery's risk of thermal runaway. This allows targeted prediction for vehicles with elevated risk rather than using a single model for all vehicles. The risk assessment involves scoring factors like driving time, battery power, temperature, and weather.

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Lithium battery thermal runaway early warning method and system using unscented Kalman filters to accurately predict and provide early warning for battery thermal runaway in lithium batteries. The method involves estimating battery state of charge (SOC) and core temperature using unscented Kalman filters. This improves estimation accuracy compared to neural networks or electrochemical models. The early warning system uses this data to predict and diagnose battery thermal runaway.

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Early warning system for large-scale energy storage batteries to predict and mitigate thermal runaway events. The system uses real-time monitoring of voltage, temperature, and expansion force of the battery cells. It predicts cell state of charge (SOC) and state of health (SOH) using learned models. This allows accurate and timely detection of abnormal conditions that can lead to thermal runaway. The system proactively reports cell anomalies to prevent catastrophic failures.

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Battery thermal runaway early warning and protection system for lithium-ion batteries. The system uses deep learning to accurately predict battery thermal runaway events. It also provides adaptive protection strategies that dynamically adjust based on real-time monitoring data and battery status. This improves protection by tailoring measures to specific conditions rather than using one-size-fits-all strategies. The system also uses partitioned monitoring to provide independent monitoring of battery groups, regions, and fault locations. This allows more granular analysis and protection.

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Power battery thermal runaway prediction and suppression integrated system, method and storage medium for accurate prediction and early suppression of power battery thermal runaway. The system uses real-time data from the battery module and cooling system along with vehicle ambient temperature. A neural network model predicts thermal runaway risk based on this data. If runaway is predicted, the cooling system parameters are optimized to suppress runaway. This allows proactive cooling adjustments to prevent runaway instead of reactively managing it.

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Method and system for predicting thermal runaway of lithium batteries based on capacitive reactance analysis. The method involves measuring the capacitive reactance of a lithium battery at specific frequencies. By monitoring the capacitive reactance curve, it can predict when a battery is approaching thermal runaway. The method uses expert databases to determine the measurement frequencies and runaway thresholds based on battery material, temperature, and charge state. The capacitive reactance measurements are made online using an impedance measurement device. This provides earlier and more reliable thermal runaway detection compared to temperature, pressure, or gas sensors.

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Waste battery thermal runaway prevention system using AI prediction model and sensor monitoring to predict and prevent thermal runaway in waste batteries. The system monitors voltage, temperature, off-gas, and humidity of waste batteries. An AI model learns from input data to predict thermal runaway probability based on voltage, temperature, and off-gas. Higher humidity increases the prediction. If thresholds are exceeded, stages of alarms are issued. The system aims to detect precursors and warn before full thermal runaway.

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Intelligent battery management system that can detect and mitigate battery faults in real-time while the battery is in use. The system uses machine learning to identify anomalies in battery behavior, extracts data containing those anomalies, modifies the training data, and retrains the AI model to better recognize and manage the faulty battery operations. This allows the system to continuously learn and adapt to battery faults as they occur, improving fault detection accuracy compared to offline methods.

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Identifying root causes of premature battery aging in vehicles and recommending mitigation strategies based on confidence levels. The technique involves monitoring battery and vehicle data to identify causes like excessive discharge during parking, high temperature operation, high charge throughput, infrequent driving, and primary power source saturation. A supervised learning algorithm compares metrics to known aging categories and identifies the most similar root causes. Mitigation actions like deactivating electronics or dimming lights are taken based on confidence levels.

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Early warning system to detect abnormal heating in battery packs of energy storage systems. The system uses a trained neural network model to predict battery temperature based on historical operation data. During real-time operation, the predicted temperature is compared with the measured temperature to determine if the battery is overheating. This allows detecting potential thermal runaway before it becomes critical. The neural network model combines long short-term memory (LSTM) and convolutional neural networks (CNN) to accurately predict temperature by extracting time and space features from battery data.

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Early warning method for battery thermal runaway that improves accuracy and promptness by using statistical analysis of temperature, voltage, and sensor disconnection data. The method involves counting the number of primary characteristics (temperature anomalies) and secondary characteristics (voltage abnormalities and sensor disconnections) at each time step. If the primary characteristic count is above zero, the secondary characteristic count is also calculated. The sum of both characteristic counts is then compared to a threshold to determine if thermal runaway is occurring. This allows early warning based on multiple indicators instead of just temperature sensors which can be damaged during runaway.

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Battery diagnosis method using AI to accurately ascertain the state of a battery exhibiting non-linearity by considering time series data like voltage, current, and temperature along with non-time series data like battery impedance. The battery state information is predicted by inputting both types of data into a trained battery prediction model. This allows more accurate battery state determination compared to just using basic measurements like current, voltage, and temperature.

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