rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Nanostructured materials engineering and characterization for battery applications
  name:
    name part: Gueye, Amadou Belal
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: This book explores the engineering and innovation of materials used in battery applications. It delves into various energy storage technologies, focusing on the development and modeling of battery components such as cathodes, anodes, electrolytes, and separators. The authors, a group of experts from various institutions, aim to provide a comprehensive overview of the state-of-the-art technologies, challenges, and future opportunities in energy storage. The book is intended for researchers, practitioners, and students in the fields of chemical sciences, physics, and engineering, offering insights into the design, modeling, and application of advanced materials for batteries.
  table of contents: Front Cover -- Nanostructured Materials Engineering and Characterization for Battery Applications -- Copyright Page -- Contents -- List of contributors -- Preface -- 1 Introduction to energy storage systems and fundamentals -- 1 Electrochemical energy storage technologies: state of the art, case studies, challenges, and opportunities -- 1.1 Introduction -- 1.2 Rechargeable commercialized batteries systems -- 1.2.1 Lead-acid battery -- 1.2.1.1 Introduction -- 1.2.1.2 Composition -- 1.2.1.3 Principles of lead-acid battery -- 1.2.2 Ni-Cd battery -- 1.2.2.1 Introduction -- 1.2.2.2 Composition
  table of contents: 1.2.2.3 Principles of Ni-Cd battery -- 1.2.2.3.1 Discharge -- 1.2.2.3.2 Charge -- 1.2.3 Ni-MH battery -- 1.2.3.1 Introduction -- 1.2.3.2 Composition of Ni-MH battery -- 1.2.3.3 Principle of Ni-MH battery -- 1.2.3.3.1 Charge -- 1.2.3.3.2 Discharge -- 1.2.4 Ni-H2 battery -- 1.2.4.1 Introduction -- 1.2.4.2 Composition -- 1.2.4.3 Principle of Ni-H2 battery -- 1.2.5 Li-ion battery -- 1.2.5.1 Introduction -- 1.2.5.2 Composition -- 1.2.5.3 Principle of lithium-ion battery -- 1.2.6 Ni-Zn batteries -- 1.2.6.1 Introduction -- 1.2.6.2 Composition -- 1.2.6.3 Principles of Ni-Zn battery -- 1.2.6.3.1 Discharge
  table of contents: 1.2.6.3.2 Charge -- 1.2.7 Ni-Fe battery -- 1.2.7.1 Introduction -- 1.2.7.2 Composition -- 1.2.7.3 Principles of Ni-Fe battery -- 1.2.7.3.1 Discharging -- 1.2.7.3.2 Charging -- 1.2.8 Application of rechargeable batteries -- 1.3 Nonrechargeable commercialized batteries systems -- 1.4 Others noncommercialized and promising battery systems -- 1.4.1 Lithium-sulfur battery -- 1.4.1.1 What makes lithium-sulfur battery a promising technology? -- 1.4.1.2 What are the problems? -- 1.4.1.3 The state of research -- 1.4.2 Lithium-O2 battery -- 1.4.2.1 What makes lithium-O2 battery a promising technology?
  table of contents: 1.4.2.2 What are the problems? -- 1.4.2.3 The state of research -- 1.4.3 Solid-state battery -- 1.4.3.1 What makes solid-state battery a promising technology? -- 1.4.3.2 What are the problems? -- 1.4.3.3 The state of research -- 1.5 Conclusions -- References -- 2 Battery modeling -- 2.1 Introduction -- 2.2 Electrochemical model -- 2.2.1 Pseudo-two-dimensional model -- 2.2.2 Single-particle model -- 2.3 Reduced-order models -- 2.3.1 Pade approximation -- 2.3.2 Balanced truncation -- 2.3.3 Proper orthogonal decomposition -- 2.3.4 Galerkin projection method -- 2.4 Equivalent circuit model
  table of contents: 2.4.1 Rint model -- 2.4.2 Thevenin model -- 2.4.3 Second-order resistance and capacitance model -- 2.4.4 PNGV model -- 2.5 Data-driven model -- 2.5.1 Grey-box modeling -- 2.5.1.1 Genetic algorithm -- 2.5.1.2 Particle swarm optimization -- 2.5.2 Black-box modeling -- 2.5.2.1 Neural network models -- 2.5.2.1.1 Artificial neural network -- 2.5.2.1.2 Recurrent neural network -- 2.5.2.2 Other machine learning models -- 2.5.2.2.1 SVM models -- 2.5.2.2.2 Extreme learning machine models -- 2.5.3 Hybrid models -- 2.5.4 Limitations of data-driven modeling -- 2.6 Summary -- References
  note: edited by Amadou Belal Gueye [and 5 more].
  note: Includes index.
  subject:
    topic: Storage batteries
    topic: Materials
  subject:
    topic: Nanostructured materials
  subject:
    topic: Accumulateurs
    topic: Matériaux
  subject:
    topic: Nanomatériaux
  classification: TK2941
  classification: 621.31/2424
  identifier: 9780323913041
  identifier: 9780323914215
  identifier: 0323914217
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OPELS
    record creation date: 240708
    record change date: 20260327180131.6
    record identifier: in00005773511
    language of cataloging:
      language term: eng