Isbn: 9781041132691 - fractional order intelligent modeling for lithium-ion batteries: theory and practice (15 risultati)

hardcover. Condizione: Very Good.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Hardback. Condizione: New. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets.With the rapid growth of electric vehicles and energy storage systems, battery technology has become critical to global energy solutions. Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice aims to provide several accurate and effective intelligent modeling algorithms for the next generation of advanced BMS. Key topics include intelligent battery modeling, fractional-order modeling, physics-informed machine learning, state estimation, and degradation analysis. By integrating AI and physics-informed machine learning techniques with fractional-order modeling methods, this book presents several innovative solutions for next-generation battery management systems.This title will serve as an invaluable resource for researchers and advanced students in the fields of transportation, energy storage, and power systems, as well as those studying electric vehicles, control theory, machine learning, and fractional calculus-based modeling.…

Hardcover. Condizione: Brand New. 160 pages. 10.00x7.00x10.24 inches. In Stock.

Hardback. Condizione: New. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets.With the rapid growth of electric vehicles and energy storage systems, battery technology has become critical to global energy solutions. Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice aims to provide several accurate and effective intelligent modeling algorithms for the next generation of advanced BMS. Key topics include intelligent battery modeling, fractional-order modeling, physics-informed machine learning, state estimation, and degradation analysis. By integrating AI and physics-informed machine learning techniques with fractional-order modeling methods, this book presents several innovative solutions for next-generation battery management systems.This title will serve as an invaluable resource for researchers and advanced students in the fields of transportation, energy storage, and power systems, as well as those studying electric vehicles, control theory, machine learning, and fractional calculus-based modeling.…

Hardcover. Condizione: new. Hardcover. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets.With the rapid growth of electric vehicles and energy storage systems, battery technology has become critical to global energy solutions. Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice aims to provide several accurate and effective intelligent modeling algorithms for the next generation of advanced BMS. Key topics include intelligent battery modeling, fractional-order modeling, physics-informed machine learning, state estimation, and degradation analysis. By integrating AI and physics-informed machine learning techniques with fractional-order modeling methods, this book presents several innovative solutions for next-generation battery management systems.This title will serve as an invaluable resource for researchers and advanced students in the fields of transportation, energy storage, and power systems, as well as those studying electric vehicles, control theory, machine learning, and fractional calculus-based modeling. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets.With the rapid growth of electric vehicles and energy storage systems, battery technology has become critical to global energy solutions. Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice aims to provide several accurate and effective intelligent modeling algorithms for the next generation of advanced BMS. Key topics include intelligent battery modeling, fractional-order modeling, physics-informed machine learning, state estimation, and degradation analysis. By integrating AI and physics-informed machine learning techniques with fractional-order modeling methods, this book presents several innovative solutions for next-generation battery management systems.This title will serve as an invaluable resource for researchers and advanced students in the fields of transportation, energy storage, and power systems, as well as those studying electric vehicles, control theory, machine learning, and fractional calculus-based modeling. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets.With the rapid growth of electric vehicles and energy storage systems, battery technology has become critical to global energy solutions. Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice aims to provide several accurate and effective intelligent modeling algorithms for the next generation of advanced BMS. Key topics include intelligent battery modeling, fractional-order modeling, physics-informed machine learning, state estimation, and degradation analysis. By integrating AI and physics-informed machine learning techniques with fractional-order modeling methods, this book presents several innovative solutions for next-generation battery management systems.This title will serve as an invaluable resource for researchers and advanced students in the fields of transportation, energy storage, and power systems, as well as those studying electric vehicles, control theory, machine learning, and fractional calculus-based modeling. This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…