Battery State Estimation Methods and Models

Format:

Book

Author/Creator:

Wang, Shunli

Series:

Energy Engineering Ser

Energy Engineering Ser.

Language:

English

Subjects (All):

Electric batteries--Mathematical models.

Electric batteries.

Kalman filtering.

Physical Description:

1 online resource

Place of Publication:

Stevenage Institution of Engineering & Technology 2021

Summary:

Batteries are vital for storing renewable energy for stationary and mobile applications. Managing batteries requires knowledge of parameters such as charge and power output. State estimation estimates such parameters using measurement and modelling; a process conveyed in this book through experimental results and verification

Contents:

Intro

Title

Copyright

Contents

About the editor

Foreword

Preface

List of contributors

Chapter 1 Introduction

1.1 State of the art

1.2 Application requirements

1.3 Research methodology

1.4 Research status and direction

1.5 Chapter summary

Acknowledgment

Chapter 2 Mechanism and influencing factors of lithium-ion batteries

2.1 Introduction

2.2 Operating mechanism

2.2.1 Brief introduction

2.2.2 Battery composition

2.2.3 Working principle

2.2.4 Cycling lifespan

2.3 Battery characteristics

2.3.1 State of power

2.3.2 Internal resistance

2.3.3 Open-circuit voltage

2.3.4 Self-discharge current rate

2.3.5 Terminal voltage

2.3.6 Current heat energy

2.3.7 Capacity variation

2.3.8 Temperature change

2.4 Critical indicators for battery state estimation

2.4.1 Description of major parameters

2.4.2 Temperature effects

2.4.3 Charge__amp__#8211

discharge current rate

2.4.4 Aging degree

2.4.5 Self-discharge rate

2.5 Basic state estimation strategies

2.5.1 Discharging test

2.5.2 Ah integral method

2.5.3 Open-circuit voltage method

2.5.4 Internal resistance method

2.6 Kalman filtering and its extension

2.6.1 Kalman filtering

2.6.2 Extended Kalman filtering

2.6.3 Unscented Kalman filtering

2.6.4 Dual Kalman filtering

2.6.5 Adaptive extended Kalman filtering

2.6.6 Square root-unscented Kalman filtering

2.6.7 Cubature Kalman filtering

2.7 Intelligent state estimation methods

2.7.1 State observer

2.7.2 Monte Carlo treatment

2.7.3 Bayesian estimation

2.7.4 Support vector machine

2.7.5 Particle filtering

2.7.6 Neural network

2.7.7 Deep learning

2.8 Algorithm improvement strategies

2.8.1 Bayesian importance sampling

2.8.2 Coordinate transformation

2.8.3 Binary iteration treatment

2.9 Chapter summary

Chapter 3 Equivalent modeling, improvement, and state-space description

3.1 Introduction

3.1.1 Application background

3.1.2 Modeling principle

3.1.3 Modeling types and concepts

3.1.4 Model building principle

3.1.5 Battery modeling methods

3.1.6 Modeling characteristic comparison

3.2 Electrochemical modeling

3.2.1 Electrochemical modeling

3.2.2 Mathematical Shepherd modeling

3.2.3 Electrochemical thermal modeling

3.3 Electrical equivalent modeling

3.3.1 Equivalent circuit modeling

3.3.2 Internal resistance modeling

3.3.3 Resistance__amp__#8211

capacitance modeling

3.3.4 Electrical modeling effect comparison

3.3.5 Surface effect modeling

3.4 Improved Thevenin equivalent modeling

3.4.1 Thevenin electrical modeling

3.4.2 Second-order circuit modeling

3.4.3 Dynamic high-order equivalent modeling

3.4.4 Double internal resistance modeling

3.4.5 Improved surface effect modeling

3.4.6 State-space description

3.4.7 Simulation realization

3.5 Improved equivalent circuit modeling

Notes:

Print version record

Other Format:

Print version Wang, Shunli Battery State Estimation

ISBN:

183953530X

9781839535307

OCLC:

1276856503

Access Restriction:

Restricted for use by site license