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Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems (en Inglés)
Wang, Shunli; Huang, Qi; Zhang, Liya (Autor)
·
Elsevier
· Tapa Blanda
Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems (en Inglés) - Wang, Shunli; Huang, Qi; Zhang, Liya
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Reseña del libro "Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems (en Inglés)"
Modelling, Estimation and AI applications for Lithium-Ion Battery Management Systems is comprehensive guide to the latest advancements in integrating artificial intelligence with lithium-ion battery technology. As the world accelerates toward carbon neutrality and a sustainable energy future, lithium-ion batteries play a pivotal role in electric transportation and energy storage systems. This book offers an in-depth exploration of fundamental principles, advanced modeling techniques, and state estimation strategies vital for enhancing battery performance, safety, and longevity. The book has a systematic coverage of battery operation, performance testing methods, and application scenarios, providing a solid foundation for understanding current challenges and innovations. The book delves into core AI algorithms, including machine learning, deep learning, and hybrid approaches, illustrating how they revolutionize battery modeling and health monitoring. Key topics include hybrid modeling methods that combine equivalent circuit models, electrochemical theories, and AI techniques; precise estimation of State of Charge (SOC), State of Health (SOH), and State of Power (SOP); and strategies for joint state estimation to facilitate comprehensive battery management. Practical insights are reinforced with detailed discussions on experimental platform design, validation procedures, and data visualization techniques, bridging theory and real-world engineering. Ideal for researchers, engineers, and practitioners in battery technology, energy storage, and intelligent energy systems, this book equips readers with the latest methodologies and trends to advance sustainable energy solutions. Whether you're developing next-generation batteries or optimizing existing systems, this authoritative resource will guide you through the innovative landscape of AI-powered battery management.
