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Enhancing Electric Vehicle Safety: A...

Das Goswami, Basab Ranjan,

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Enhancing Electric Vehicle Safety: AI-Driven Multiphysics Approach for Predicting Thermal Failures in Li-Ion Batteries /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhancing Electric Vehicle Safety: AI-Driven Multiphysics Approach for Predicting Thermal Failures in Li-Ion Batteries // Basab Ranjan Das Goswami.
Author:	Das Goswami, Basab Ranjan,
Description:	1 electronic resource (125 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
Subject:	Energy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31488834
ISBN:	9798383694244

Enhancing Electric Vehicle Safety: AI-Driven Multiphysics Approach for Predicting Thermal Failures in Li-Ion Batteries /
Das Goswami, Basab Ranjan,

Enhancing Electric Vehicle Safety: AI-Driven Multiphysics Approach for Predicting Thermal Failures in Li-Ion Batteries /Basab Ranjan Das Goswami. - 1 electronic resource (125 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

The focus of this dissertation is to address the critical issue of safety in lithium-ion batteries (LIBs), particularly focusing on thermal runaway (TR), a significant threat to their reliability and widespread adoption. With the surge in demand for energy storage driven by electric vehicles (EVs) and renewable energy uptake, ensuring LIB safety is paramount. The research innovatively combines advanced machine learning (ML) techniques with multiphysics modeling to predict and prevent TR incidents. Initially, I applied convolutional neural networks (CNNs) and the "you look only once" (YOLO) object detection model to forecast TR in single LIBs. This phase leveraged simulated thermal images generated by a comprehensive multiphysics model that incorporates thermal, electrochemical, and degradation processes, particularly focusing on solid electrolyte interface (SEI) dynamics. The models demonstrated high accuracy in predicting TR stages and pinpointing heat sources within a battery. Expanding this approach, the I, then developed a novel ML framework to address TR in LIB modules, which is crucial for EV safety. This framework integrates graph neural networks (GNN) for spatial analysis and Long Short-Term Memory (LSTM) networks for temporal predictions, which are trained on sensor-derived temperature data. The advanced model effectively identified spatio-temporal temperature variations and potential hotspots within battery modules, offering real-time insights to avert TR. This research significantly advances the safety and reliability of LIBs by integrating cutting-edge ML with detailed multiphysics modeling. It marks a substantial step towards safer, more sustainable energy storage solutions, contributing to the broader goal of a low-carbon future by enhancing LIB integration in diverse applications, from portable electronics to EVs and renewable energy systems.

English

ISBN: 9798383694244Subjects--Topical Terms:

876794
Energy.
Subjects--Index Terms:

Battery degradation

Enhancing Electric Vehicle Safety: AI-Driven Multiphysics Approach for Predicting Thermal Failures in Li-Ion Batteries /
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520 $a The focus of this dissertation is to address the critical issue of safety in lithium-ion batteries (LIBs), particularly focusing on thermal runaway (TR), a significant threat to their reliability and widespread adoption. With the surge in demand for energy storage driven by electric vehicles (EVs) and renewable energy uptake, ensuring LIB safety is paramount. The research innovatively combines advanced machine learning (ML) techniques with multiphysics modeling to predict and prevent TR incidents. Initially, I applied convolutional neural networks (CNNs) and the "you look only once" (YOLO) object detection model to forecast TR in single LIBs. This phase leveraged simulated thermal images generated by a comprehensive multiphysics model that incorporates thermal, electrochemical, and degradation processes, particularly focusing on solid electrolyte interface (SEI) dynamics. The models demonstrated high accuracy in predicting TR stages and pinpointing heat sources within a battery. Expanding this approach, the I, then developed a novel ML framework to address TR in LIB modules, which is crucial for EV safety. This framework integrates graph neural networks (GNN) for spatial analysis and Long Short-Term Memory (LSTM) networks for temporal predictions, which are trained on sensor-derived temperature data. The advanced model effectively identified spatio-temporal temperature variations and potential hotspots within battery modules, offering real-time insights to avert TR. This research significantly advances the safety and reliability of LIBs by integrating cutting-edge ML with detailed multiphysics modeling. It marks a substantial step towards safer, more sustainable energy storage solutions, contributing to the broader goal of a low-carbon future by enhancing LIB integration in diverse applications, from portable electronics to EVs and renewable energy systems.
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