Elsevier Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems

Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems is a comprehensive guide to the latest advancements in integrating artificial intelligence with lithium-ion battery technology. The book offers an in-depth exploration of fundamental principles, advanced modeling techniques, and state estimation strategies that are vital for enhancing battery performance, safety, and longevity. The book presents systematic coverage of battery operation, performance testing methods, and application scenarios, providing a solid foundation for understanding current challenges and innovations. Sections delve 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. Utilizes machine learning and deep learning as the core driving force, integrating traditional equivalent circuit models with data-driven models to overcome the limitations of single physical modeling or scenario constraints Introduces the SOP (State-Of-Power) Estimation and multi-state joint optimization framework to address coupling defects inherent in traditional independent estimation methods Teaches how to build a comprehensive chain encompassing “AI algorithm foundation ― multi-model fusion ― full lifecycle management ― experimental verification” to ensure robustness and reliability of Li-ion batteries