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Mathematical modeling of lithium bat...

Hariharan, Krishnan S.

Mathematical modeling of lithium batteriesfrom electrochemical models to state estimator algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical modeling of lithium batteriesby Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran.
Reminder of title:	from electrochemical models to state estimator algorithms /
Author:	Hariharan, Krishnan S.
other author:	Tagade, Piyush.
Published:	Cham :Springer International Publishing :2018.
Description:	xiv, 211 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical modelsData processing.
Online resource:	http://dx.doi.org/10.1007/978-3-319-03527-7
ISBN:	9783319035277$q(electronic bk.)

Mathematical modeling of lithium batteriesfrom electrochemical models to state estimator algorithms /
Hariharan, Krishnan S.

Mathematical modeling of lithium batteriesfrom electrochemical models to state estimator algorithms /[electronic resource] :by Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran. - Cham :Springer International Publishing :2018. - xiv, 211 p. :ill., digital ;24 cm. - Green energy and technology,1865-3529. - Green energy and technology..

Lithium batteries and underlying electrochemical processes -- Electrochemical model (EM) for lithium batteries -- Electrochemical impedance spectroscopy (EIS) models -- Equivalent circuit models (ECM) -- Reduced order models -- Battery management system - state estimator and algorithms -- Battery thermal models -- Battery life models.

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.

ISBN: 9783319035277$q(electronic bk.)

Standard No.: 10.1007/978-3-319-03527-7doiSubjects--Topical Terms:

803394
Mathematical models
--Data processing.

LC Class. No.: QA401 / .H375 2018

Dewey Class. No.: 621.3126

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245 1 0 $a Mathematical modeling of lithium batteries $h [electronic resource] : $b from electrochemical models to state estimator algorithms / $c by Krishnan S. Hariharan, Piyush Tagade, Sanoop Ramachandran.
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505 0 $a Lithium batteries and underlying electrochemical processes -- Electrochemical model (EM) for lithium batteries -- Electrochemical impedance spectroscopy (EIS) models -- Equivalent circuit models (ECM) -- Reduced order models -- Battery management system - state estimator and algorithms -- Battery thermal models -- Battery life models.
520 $a 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.
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