Battery State Estimation : Methods and Models.

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 experiment...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wang, Shunli, 1985-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Stevenage : Institution of Engineering & Technology, 2022.
Colección:Energy Engineering Ser.
Temas:
Electric batteries > Mathematical models.
Electric batteries.
Piles électriques > Modèles mathématiques.
Piles électriques.
batteries (electrical)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • Acknowledgment
  • 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
  • 3.5.1 Runtime electrical modeling
  • 3.5.2 Fractional-order electrical model
  • 3.5.3 Improved Thevenin model
  • 3.5.4 State-space description
  • 3.5.5 Simulation realization
  • 3.6 High-order model establishment
  • 3.6.1 High-order electrical modeling
  • 3.6.2 Ohmic resistance identification
  • 3.6.3 State-space expression
  • 3.7 Model parameter description
  • 3.7.1 Ampere-hour counting
  • 3.7.2 Exponential curve fitting
  • 3.7.3 Recursive least square
  • 3.7.4 Full model parameter identification
  • 3.8 Chapter summary
  • Acknowledgment
  • Chapter 4 Extended Kalman filtering and its extension
  • 4.1 Kalman filtering extension strategies
  • 4.1.1 Kalman filtering algorithm
  • 4.1.2 Extended Kalman filtering
  • 4.1.3 Fractional-order adaptive correction.
  • 4.2 Equivalent circuit modeling
  • 4.2.1 Second-order Thevenin modeling
  • 4.2.2 Identification procedure design
  • 4.2.3 Identification effect verification
  • 4.2.4 Corroboration of model parameters
  • 4.3 Model parameter identification
  • 4.3.1 Recursive least-square calculation
  • 4.3.2 Forgetting factor__amp__#8212
  • RLS algorithm
  • 4.3.3 Adaptive PSO
  • 4.3.4 Parameter extraction results
  • 4.4 Fractional experimental test
  • 4.4.1 Real-time platform implementation
  • 4.4.2 Test step procedure
  • 4.4.3 HPPC test
  • 4.4.4 Capacity tracking experiments
  • 4.5 Extended Kalman filtering-based state of charge estimation
  • 4.5.1 State of charge determination
  • 4.5.2 Application requirements
  • 4.5.3 Time-varying correction
  • 4.5.4 Simulation interfacing process
  • 4.5.5 Pulse-current estimation effect verification
  • 4.5.6 Estimation for BBDST conditions
  • 4.6 EKF-based state of health estimation
  • 4.6.1 Estimation model establishment
  • 4.6.2 Model parameter verification
  • 4.6.3 State of health estimation for the HPPC test
  • 4.6.4 State of health variation for BBDST
  • 4.6.5 State of health estimation of dynamic stress test
  • 4.6.6 State of health estimation with capacity fade
  • 4.7 Chapter summary
  • Acknowledgment
  • Chapter 5 Adaptive extended Kalman filtering for multiple battery state estimation
  • 5.1 Introduction
  • 5.2 Iterative calculation strategies
  • 5.2.1 Iterative predicting-updating calculation
  • 5.2.2 Nonlinear state-space extension
  • 5.2.3 Estimation model construction
  • 5.2.4 Adaptive extended Kalman filtering
  • 5.2.5 Improved adaptive extended Kalman filtering
  • 5.3 Parameter identification
  • 5.3.1 Test platform construction
  • 5.3.2 Parameter identification procedure
  • 5.3.3 Parameter varying law extraction
  • 5.3.4 Capacity test results
  • 5.3.5 HPPC test results
  • 5.3.6 Open-circuit voltage tests.
  • 5.3.7 Combined capacity and HPPC tests
  • 5.4 State of charge estimation
  • 5.4.1 Simulated estimation results
  • 5.4.2 Voltage traction effect
  • 5.4.3 Pulse-current estimation verification
  • 5.4.4 BBDST estimation results
  • 5.5 State of power prediction
  • 5.5.1 State of power characteristics
  • 5.5.2 SOC-based SOP estimation
  • 5.5.3 EEC-based SOP estimation
  • 5.5.4 Multi-constraint SOP estimation
  • 5.5.5 BBDST estimation results
  • 5.6 Chapter summary
  • Acknowledgment
  • Chapter 6 Dual extended Kalman filtering prediction for complex working conditions
  • 6.1 Introduction
  • 6.2 Aging modeling methods
  • 6.2.1 Aging mechanisms
  • 6.2.2 Electrochemical aging models
  • 6.2.3 Analytical aging models
  • 6.2.4 Equivalent circuit aging models
  • 6.2.5 Statistical aging models
  • 6.2.6 Battery aging model
  • 6.2.7 Internal resistance growth
  • 6.2.8 Mathematical aging models
  • 6.3 Iterative calculation algorithm
  • 6.3.1 Cyclic aging expression
  • 6.3.2 Rain-flow counting
  • 6.3.3 Cyclic charge__amp__#8211
  • discharge variation
  • 6.3.4 State of safety analysis
  • 6.3.5 Definitions of key points
  • 6.3.6 Electrical equivalent circuit modeling
  • 6.3.7 Model parameter identification
  • 6.3.8 Dual extended Kalman filtering
  • 6.4 Parameter test and identification
  • 6.4.1 Experimental platform setup
  • 6.4.2 Whole-life-cycle HPPC test
  • 6.4.3 Capacity characterization test
  • 6.4.4 Open-circuit voltage test
  • 6.4.5 Recursive least-square method
  • 6.5 Complex condition experiment
  • 6.5.1 Test platform construction
  • 6.5.2 Cyclic aging procedure design
  • 6.5.3 Battery aging modeling results
  • 6.5.4 Parameter identification results
  • 6.5.5 BBDST verification
  • 6.5.6 Estimation result verification
  • 6.6 Chapter summary
  • Acknowledgment
  • Chapter 7 Unscented particle filtering of safety estimation considering capacity fading effect.
  • 7.1 Introduction
  • 7.2 Capacity fade modeling methods
  • 7.2.1 Capacity fade mechanism
  • 7.2.2 Capacity fade modeling methods
  • 7.2.3 Capacity fade modeling
  • 7.2.4 Mathematical knee point expression
  • 7.2.5 Arrhenius model quantification
  • 7.2.6 Knee-Arrhenius expression
  • 7.3 Estimation modeling methods
  • 7.3.1 Equivalent circuit modeling
  • 7.3.2 Improved PNGV circuit modeling
  • 7.3.3 High-order modeling realization
  • 7.3.4 Internal resistance estimation
  • 7.3.5 Parameter identification
  • 7.3.6 Particle filtering algorithm
  • 7.3.7 Unscented Kalman filtering
  • 7.3.8 Improved unscented particle filtering
  • 7.4 Experimental result analysis
  • 7.4.1 Test platform construction
  • 7.4.2 Open-circuit voltage characterization
  • 7.4.3 Capacity fade modeling effect
  • 7.4.4 State of safety evaluation
  • 7.4.5 Parameter identification tests
  • 7.4.6 State estimation effect verification
  • 7.5 Chapter summary
  • Acknowledgment
  • References
  • Index.

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