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Smart Battery Management for Enhanced Safety [electronic resource] / by Zhongbao Wei.

By: Contributor(s): Material type: TextSeries: Key Technologies on New Energy VehiclesPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XXI, 230 p. 110 illus., 102 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789819746392
Subject(s):
Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • TA329-348
Online resources:
Contents:
Overview of battery management -- Modeling -- State Estimation -- Health Diagnostic -- Charging -- Smart battery and management.
In: Springer Nature eBookSummary: This book consolidates studies in the rapidly and foreseeably growing field of battery management. The primary focus is to overview the management of batteries (Li-ion batteries and some cases of flow batteries) with the fusion of mechanism and AI-based approaches. The book can be categorized into three groups, i.e., (i) mechanism and AI-based battery modeling and parameterization, (ii) AI-based diagnostic, early warning, and active safety control, and (iii) emerging techniques of smart battery and smart management, combining the emerging areas of embedded sensing and reconfigurable batteries. It is well recognized that the battery safety and management are the kernel of energy storage, renewable utilization, and low-carbon society, which have been highly popular in recent years. The exploration of AI techniques for advanced battery management has been seldom discussed systematically before. Moreover, the combination of AI and mechanism approaches can remarkably enhance the battery management, which however has never been focused on in previous books. Therefore, this book can add new knowledge to the paradigm and attract the attention of academics, scientists, engineers, and practitioners. It is a reference book for researchers and engineers in related fields. The step-by-step guidance, comprehensive introduction, and case studies make it accessible to audiences of different levels, from graduates to experienced engineers.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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Overview of battery management -- Modeling -- State Estimation -- Health Diagnostic -- Charging -- Smart battery and management.

This book consolidates studies in the rapidly and foreseeably growing field of battery management. The primary focus is to overview the management of batteries (Li-ion batteries and some cases of flow batteries) with the fusion of mechanism and AI-based approaches. The book can be categorized into three groups, i.e., (i) mechanism and AI-based battery modeling and parameterization, (ii) AI-based diagnostic, early warning, and active safety control, and (iii) emerging techniques of smart battery and smart management, combining the emerging areas of embedded sensing and reconfigurable batteries. It is well recognized that the battery safety and management are the kernel of energy storage, renewable utilization, and low-carbon society, which have been highly popular in recent years. The exploration of AI techniques for advanced battery management has been seldom discussed systematically before. Moreover, the combination of AI and mechanism approaches can remarkably enhance the battery management, which however has never been focused on in previous books. Therefore, this book can add new knowledge to the paradigm and attract the attention of academics, scientists, engineers, and practitioners. It is a reference book for researchers and engineers in related fields. The step-by-step guidance, comprehensive introduction, and case studies make it accessible to audiences of different levels, from graduates to experienced engineers.

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