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Battery State Estimation : = Methods and Models.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Battery State Estimation :/
其他題名:	Methods and Models.
作者:	Wang, Shunli.
出版者:	Stevenage :Institution of Engineering & Technology, : 2021.,
面頁冊數:	1 online resource (313 p.).
標題:	Filtre de Kalman. -
電子資源:	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3058727
ISBN:	183953530X

Battery State Estimation : = Methods and Models.
Wang, Shunli.

Battery State Estimation :Methods and Models. - Stevenage :Institution of Engineering & Technology,2021. - 1 online resource (313 p.). - Energy Engineering Ser.. - Energy Engineering Ser..

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

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.

ISBN: 183953530XSubjects--Topical Terms:

1457782
Filtre de Kalman.

LC Class. No.: TK2896

Dewey Class. No.: 621.31242

Battery State Estimation : = Methods and Models.
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505 0 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 3.5 Improved equivalent circuit modeling.
520 $a 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.
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650 0 $a Kalman filtering. $3 579841
650 0 $a Electric batteries $x Mathematical models. $3 1219020
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