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Mathematical Modeling of Lithium Batteries

From Electrochemical Models to State Estimator Algorithms

  • Krishnan S. Hariharan
  • Piyush Tagade
  • Sanoop Ramachandran

Part of the Green Energy and Technology book series (GREEN)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
    Pages 1-9
  3. The Electrochemical Thermal Model

    1. Front Matter
      Pages 11-11
    2. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 13-32
    3. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 33-43
    4. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 45-60
  4. Reduced Order Models

    1. Front Matter
      Pages 61-61
    2. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 63-103
    3. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 105-125
  5. State and Health Estimation

    1. Front Matter
      Pages 127-127
    2. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 129-144
    3. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 145-173
    4. Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran
      Pages 175-203
  6. Back Matter
    Pages 205-211

About this book

Introduction

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. The authors 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 health estimation 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.


Keywords

Battery Management Systems and State Estimators Battery Thermal Analysis Electrochemical Impedance Spectroscopy Lithium Batteries Mathematical Modeling of Batteries Modeling Batteries

Authors and affiliations

  • Krishnan S. Hariharan
    • 1
  • Piyush Tagade
    • 2
  • Sanoop Ramachandran
    • 3
  1. 1.Samsung Advanced Institute of Technology (SAIT)-IndiaSamsung R&D Institute IndiaBangaloreIndia
  2. 2.Samsung Advanced Institute of Technology (SAIT)-IndiaSamsung R&D Institute IndiaBangaloreIndia
  3. 3.Samsung Advanced Institute of Technology (SAIT)-IndiaSamsung R&D Institute IndiaBangaloreIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-03527-7
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Energy
  • Print ISBN 978-3-319-03526-0
  • Online ISBN 978-3-319-03527-7
  • Series Print ISSN 1865-3529
  • Series Online ISSN 1865-3537
  • Buy this book on publisher's site