Prevent Thermal Runaway in EV using Machine Learning

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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Detecting high impact scenarios using machine learning by iteratively generating test inputs with feature variations, simulating with the ML model to find output variations, and optimizing for maximized output variation with minimized input variation. This involves iteratively generating test inputs with feature variations, simulating with the ML model to find output variations, and using optimization to find the input feature that causes the most output variation with the least input variation. This allows identifying inputs that drastically change the ML output versus those with small changes.

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Determining failure of a vehicle component using trained artificial intelligence models to detect anomalies in measured signals. The method involves retrieving input signals correlated with the vehicle signal, predicting the signal using the AI model, determining a tolerance band, checking model validity, retrieving the actual signal, and determining if it's an anomaly based on the predicted, tolerance, and model validity. Counting and storing anomaly occurrences, and indicating component failure if a threshold of anomalies exceeds over time.

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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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