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Battery State Estimation : Methods and Models
Battery State Estimation : Methods and Models
Autore Wang Shunli <1985->
Edizione [1st ed.]
Pubbl/distr/stampa Stevenage : , : Institution of Engineering & Technology, , 2022
Descrizione fisica 1 online resource (313 pages)
Disciplina 621.31242
Collana Energy Engineering
Soggetto topico Electric batteries - Mathematical models
Electric batteries
ISBN 1-83724-580-0
1-5231-4238-3
1-83953-530-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Altri titoli varianti Battery State Estimation
Record Nr. UNINA-9911007060103321
Wang Shunli <1985->  
Stevenage : , : Institution of Engineering & Technology, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computer aided engineering of batteries / / edited by Shriram Santhanagopalan
Computer aided engineering of batteries / / edited by Shriram Santhanagopalan
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (289 pages)
Disciplina 620.00420285
Collana Modern Aspects of Electrochemistry
Soggetto topico Computer-aided engineering
Electric batteries - Mathematical models
ISBN 9783031176074
9783031176067
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applications of Commercial Software for Lithium-Ion Battery Modeling and Simulation -- In situ Measurement of Current Distribution in Large-format Li-ion Cells -- Mesoscale Modeling and Analysis in Electrochemical Energy Systems -- Development of Computer Aided Design Tools for Automotive Batteries -- Experimental Simulations of Field Induced Mechanical Abuse Conditions -- Abuse Response of Batteries subjected to Mechanical Impact -- Accelerating Battery Simulations by using High Performance Computing and Opportunities with Machine Learning. .
Record Nr. UNISA-996546821403316
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Progress in modeling and simulation of batteries / / edited by John A. Turner
Progress in modeling and simulation of batteries / / edited by John A. Turner
Autore Turner John
Pubbl/distr/stampa Warrendale, Pa. (400 Commonwealth Dr., Wallendale PA USA) : , : Society of Automotive Engineers, , 2016
Descrizione fisica 1 online resource (98 pages)
Disciplina 621.31242
Collana Society of Automotive Engineers. Electronic publications.
Soggetto topico Electric batteries - Mathematical models
ISBN 1-5231-2420-2
0-7680-8366-4
0-7680-8725-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- 1. Characterizing thermal behavior of an air-cooled lithium-ion battery system for HEV applications using FEA approach (2013-01-1520) -- 2. AutoLion: a thermally coupled simulation tool for automotive li-ion batteries (2013-01-1522) -- 3. Simplified extended Kalman filter observer for SOC estimation of commercial power-oriented LFP lithium battery cells (2013-01-1544) -- 4. A complete li-ion battery simulation model (2014-01-1842) -- Comparison of optimization techniques for lithium-ion battery model parameter estimation (2014-01-1851) -- 6. Physics-based models, sensitivity analysis, and optimization of automotive batteries (2014-01-1865) -- 7. Three-dimensional electrochemical analysis of a graphite-LiFePO4 li-ion cell to improve its durability (2015-01-1182) -- 8. Experimental measurements of thermal characteristics of LiFePO4 battery (2015-01-1189) -- 9. Will your battery survive a world with fast chargers? (2015-01-1196) -- About the editor.
Record Nr. UNINA-9910438217003321
Turner John  
Warrendale, Pa. (400 Commonwealth Dr., Wallendale PA USA) : , : Society of Automotive Engineers, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui