Thermal Management for Batteries

Thermal Management of Batteries presents a comprehensive examination of the various conventional and emerging technologies used for thermal management of batteries and electronics. With an emphasis on advanced nanofluids, the book provides step-by-step guidance on advanced techniques at the component and system level for both active and passive technologyStarting with an overview of the fundamentals, each chapter quickly builds into a comprehensive treatment of up-to-date technologies. The first part of the book discusses advanced battery technologies, while the second part addresses the design and performance optimization of battery thermal management systems. Power density and fast charging mechanisms of batteries are considered, as are role of thermal management systems on performance enhancement. The book discusses the design selection of various thermal management systems, parameters selection for different configurations, the operating conditions for different battery types, the setups used for experimentation and instrumentation, and the operation of thermal management systems. Advanced techniques such as heat pipes, phase change materials, nanofluids, novel heat sinks, and two phase flow loops are examined in detail.Presenting the fundamentals through to the latest developments alongside step-by-step guidance, mathematical models, schematic diagrams, and experimental data, Thermal Management of Batteries is an invaluable and comprehensive reference for graduates, researchers, and practicing engineers working in the field of battery thermal management, and offers valuable solutions to key thermal management problems that will be of interest to anyone working on energy and thermal heat systems.

• Critically examines the components of batteries systems and their thermal energy generation
• Analyzes system scale integration of battery components with optimization and better design impact
• Explores the modeling aspects and applications of nanofluid technology and PCMs, as well as the utilization of machine learning techniques
• Provides step-by-step guidance on techniques in each chapter that are supported by mathematical models, schematic diagrams, and experimental data

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