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As the global transition toward carbon neutrality accelerates, Lithium-ion batteries (LIBs) have become the cornerstone of electric mobility and renewable energy storage. However, ensuring their safety and maximizing their lifespan requires precise solutions for complex electrochemical and thermal challenges. This Reprint, representing the second edition of the Special Issue "Modeling, Reliability, and Health Management of Lithium-Ion Batteries," compiles eight significant research contributions and a comprehensive editorial that define the current state of the art in this field. The collected works address three interconnected themes. In the domain of modeling, authors introduce efficient parameter extraction methods for equivalent circuit models and solid-phase diffusion models for high-power LTO batteries. Reliability and safety are addressed through novel investigations into thermal runaway mechanisms, including pressure dynamics in prismatic cells and environmental impact evaluations, alongside robust fault diagnosis techniques utilizing relative entropy. Finally, the Reprint showcases breakthroughs in health management, featuring physics-guided machine learning for capacity fading prediction, autoencoder-based SOH estimation for small data samples.
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Modeling, Reliability and Health Management of Lithium-Ion Batteries: 2nd Edition
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Hardcover. Condizione: new. Hardcover. As the global transition toward carbon neutrality accelerates, Lithium-ion batteries (LIBs) have become the cornerstone of electric mobility and renewable energy storage. However, ensuring their safety and maximizing their lifespan requires precise solutions for complex electrochemical and thermal challenges. This Reprint, representing the second edition of the Special Issue "Modeling, Reliability, and Health Management of Lithium-Ion Batteries," compiles eight significant research contributions and a comprehensive editorial that define the current state of the art in this field. The collected works address three interconnected themes. In the domain of modeling, authors introduce efficient parameter extraction methods for equivalent circuit models and solid-phase diffusion models for high-power LTO batteries. Reliability and safety are addressed through novel investigations into thermal runaway mechanisms, including pressure dynamics in prismatic cells and environmental impact evaluations, alongside robust fault diagnosis techniques utilizing relative entropy. Finally, the Reprint showcases breakthroughs in health management, featuring physics-guided machine learning for capacity fading prediction, autoencoder-based SOH estimation for small data samples. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9783725861996
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As the global transition toward carbon neutrality accelerates, Lithium-ion batteries (LIBs) have become the cornerstone of electric mobility and renewable energy storage. However, ensuring their safety and maximizing their lifespan requires precise solutions for complex electrochemical and thermal challenges. This Reprint, representing the second edition of the Special Issue 'Modeling, Reliability, and Health Management of Lithium-Ion Batteries,' compiles eight significant research contributions and a comprehensive editorial that define the current state of the art in this field. The collected works address three interconnected themes. In the domain of modeling, authors introduce efficient parameter extraction methods for equivalent circuit models and solid-phase diffusion models for high-power LTO batteries. Reliability and safety are addressed through novel investigations into thermal runaway mechanisms, including pressure dynamics in prismatic cells and environmental impact evaluations, alongside robust fault diagnosis techniques utilizing relative entropy. Finally, the Reprint showcases breakthroughs in health management, featuring physics-guided machine learning for capacity fading prediction, autoencoder-based SOH estimation for small data samples. Codice articolo 9783725861996
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