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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. vii 7. Chapter One. Overview and applications of lithium-ion batteries - p. 1 8. 1.1 Operating principles of lithium-ion batteries - p. 1 9. 1.2 Performance testing methods - p. 9 10. 1.3 Performance characteristics - p. 36 11. 1.4 Application scenarios - p. 87 12. Chapter Two. Fundamentals of artificial intelligence and algorithms - p. 103 13. Chapter Three. Modeling methods for lithium-ion batteries - p. 153 14. Chapter Four. State of charge (SOC) estimation - p. 183 15. Chapter Five. State of health (SOH) estimation - p. 215 16. Chapter Six. State of power (SOP) estimation - p. 225 17. Chapter Seven. Joint estimation of battery states - p. 265 18. Chapter Eight. Experimental design and validation - p. 305 19. Chapter Nine. Future directions and societal impact - p. 335 20. Index - p. 353
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. vii 7. Chapter One. Overview and applications of lithium-ion batteries - p. 1 8. 1.1 Operating principles of lithium-ion batteries - p. 1 9. 1.2 Performance testing methods - p. 9 10. 1.3 Performance characteristics - p. 36 11. 1.4 Application scenarios - p. 87 12. Chapter Two. Fundamentals of artificial intelligence and algorithms - p. 103 13. Chapter Three. Modeling methods for lithium-ion batteries - p. 153 14. Chapter Four. State of charge (SOC) estimation - p. 183 15. Chapter Five. State of health (SOH) estimation - p. 215 16. Chapter Six. State of power (SOP) estimation - p. 225 17. Chapter Seven. Joint estimation of battery states - p. 265 18. Chapter Eight. Experimental design and validation - p. 305 19. Chapter Nine. Future directions and societal impact - p. 335 20. Index - p. 353
