Overview
- Evaluates and discusses various approaches to form one cohesive methodology for application and future development
- Considers recent trends and research to explore their potential and limitations to develop technology and efficient practices
- Provides a much-needed coherent framework of battery modeling techniques
- Includes supplementary material: sn.pub/extras
Part of the book series: Green Energy and Technology (GREEN)
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Table of contents (9 chapters)
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Front Matter
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The Electrochemical Thermal Model
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Front Matter
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Reduced Order Models
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Front Matter
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State and Health Estimation
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Front Matter
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Back Matter
Keywords
About this book
This book is unique to be the only one completely dedicated for battery modeling for all components of battery management system (BMS) applications. The contents of this book compliment the multitude of research publications in this domain by providing coherent fundamentals. An explosive market of Li ion batteries has led to aggressive demand for mathematical models for battery management systems (BMS). Researchers from multi-various backgrounds contribute from their respective background, leading to a lateral growth. Risk of this runaway situation is that researchers tend to use an existing method or algorithm without in depth knowledge of the cohesive fundamentals—often misinterpreting the outcome. It is worthy to note that the guiding principles are similar and the lack of clarity impedes a significant advancement. A repeat or even a synopsis of all the applications of battery modeling albeit redundant, would hence be a mammoth task, and cannot be done in a single offering. Theauthors believe that a pivotal contribution can be made by explaining the fundamentals in a coherent manner. Such an offering would enable researchers from multiple domains appreciate the bedrock principles and forward the frontier.
Battery is an electrochemical system, and any level of understanding cannot ellipse this premise. The common thread that needs to run across—from detailed electrochemical models to algorithms used for real time estimation on a microchip—is that it be physics based. Build on this theme, this book has three parts. Each part starts with developing a framework—often invoking basic principles of thermodynamics or transport phenomena—and ends with certain verified real time applications. The first part deals with electrochemical modeling and the second with model order reduction. Objective of a BMS is estimation of state and health, and the third part is dedicated for that. Rules for state observers are derived from a generic Bayesian framework, and healthestimation is pursued using machine learning (ML) tools. A distinct component of this book is thorough derivations of the learning rules for the novel ML algorithms. Given the large-scale application of ML in various domains, this segment can be relevant to researchers outside BMS domain as well.
The authors hope this offering would satisfy a practicing engineer with a basic perspective, and a budding researcher with essential tools on a comprehensive understanding of BMS models.
Authors and Affiliations
About the authors
Piyush Tagade is a Research Staff Member at Samsung Advanced Institute of Technology, Samsung R&D Institute, Bangalore, India. He holds a PhD degree in Aerospace Engineering from Indian Institute of Technology Bombay, India. Before joining Samsung, he was a postdoctoral research associate at Korea Advanced Institute of Science and Technology, Republic of Korea and Massachusetts Institute of Technology, USA. In his scientific research work he is mostly concerned with developing efficient Bayesian framework for large-scale system simulators. His areas of interest include Bayesian inference, uncertainty propagation, data assimilation, optimization and machine learning.
Sanoop Ramachandran was born in Kerala, India in 1981. He got his BSc degree (2001) from the University of Calicut, Kerala, India. He obtained a Masters degree (2003) and PhD (2009) in Physics from the Indian Institute of Technology Madras, India. This was followed by two postdoctoral stints at the Tokyo Metropolitan University (2011), Tokyo, Japan and the Universite Libre de Bruxelles (2012), Brussels, Belgium. From late 2012 till date, he has been working as a Staff research scientist at the Samsung R&D Institute, Bangalore, India. He is an author of over 20 journal publications, several patents ideas and book chapters. His general research interests are in the field of soft-matter, electrochemistry as well as the use of mathematical modelling and computational tools for applied industrial research.
Bibliographic Information
Book Title: Mathematical Modeling of Lithium Batteries
Book Subtitle: From Electrochemical Models to State Estimator Algorithms
Authors: Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
Series Title: Green Energy and Technology
DOI: https://doi.org/10.1007/978-3-319-03527-7
Publisher: Springer Cham
eBook Packages: Energy, Energy (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-03526-0Published: 18 January 2018
Softcover ISBN: 978-3-319-79138-8Published: 06 June 2019
eBook ISBN: 978-3-319-03527-7Published: 28 December 2017
Series ISSN: 1865-3529
Series E-ISSN: 1865-3537
Edition Number: 1
Number of Pages: XIV, 211
Number of Illustrations: 39 b/w illustrations, 34 illustrations in colour
Topics: Energy Storage, Energy Systems, Electrical Engineering