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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
Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs / / by Qi Huang, Shunli Wang, Zonghai Chen, Ran Xiong, Carlos Fernandez, Daniel-I. Stroe
Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs / / by Qi Huang, Shunli Wang, Zonghai Chen, Ran Xiong, Carlos Fernandez, Daniel-I. Stroe
Autore Huang Qi
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (101 pages)
Disciplina 621.3126
Altri autori (Persone) WangShunli
ChenZonghai
XiongRan
FernandezCarlos
StroeDaniel-I
Soggetto topico Energy storage
Electronics - Materials
Mathematical models
Mechanical and Thermal Energy Storage
Electronic Materials
Mathematical Modeling and Industrial Mathematics
ISBN 9789819953448
9819953448
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1 Introduction -- Chapter 2 Electrochemical modeling of energy storage lithium battery -- Chapter 3 Extraction of multidimensional health indicators based on lithium-ion batteries -- Chapter 4 Research on health state estimation method of the lithium-ion battery pack -- Chapter 5 Experimental verification and analysis of health state estimation for lithium-ion battery pack.
Record Nr. UNINA-9910741167403321
Huang Qi  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Proceedings of 2024 International Conference on Smart Electrical Grid and Renewable Energy (SEGRE 2024) : Volume 1 / / edited by Fushuan Wen, Haoming Liu, Huiqing Wen, Shunli Wang
Proceedings of 2024 International Conference on Smart Electrical Grid and Renewable Energy (SEGRE 2024) : Volume 1 / / edited by Fushuan Wen, Haoming Liu, Huiqing Wen, Shunli Wang
Autore Wen Fushuan
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (745 pages)
Disciplina 321.319
Altri autori (Persone) LiuHaoming
WenHuiqing
WangShunli
Collana Lecture Notes in Electrical Engineering
Soggetto topico Electric power distribution
Electric power production
Energy storage
Renewable energy sources
Energy Grids and Networks
Electrical Power Engineering
Mechanical and Thermal Energy Storage
Renewable Energy
ISBN 9789819624560
9789819624553
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- A novel load forecasting method based on TCN-BiGRU model -- Power Loss Regulation Based on Elastic Capacitance Voltage for MMCs in Photovoltaic Inverter in Application -- Fast Power Adjustment Characteristics of Double fed Variable Speed Pumped Storage Units -- Simulation and Analysis on Electromagnetic Mechanical Characteristics of Transformer -- Dynamic response of Three Phase Enclosure Type GIL Under Electromagnetic Forces, etc.
Record Nr. UNINA-9910984585903321
Wen Fushuan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Proceedings of 2024 International Conference on Smart Electrical Grid and Renewable Energy (SEGRE 2024) : Volume 2 / / edited by Fushuan Wen, Haoming Liu, Huiqing Wen, Shunli Wang
Proceedings of 2024 International Conference on Smart Electrical Grid and Renewable Energy (SEGRE 2024) : Volume 2 / / edited by Fushuan Wen, Haoming Liu, Huiqing Wen, Shunli Wang
Autore Wen Fushuan
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (1188 pages)
Disciplina 321.319
Altri autori (Persone) LiuHaoming
WenHuiqing
WangShunli
Collana Lecture Notes in Electrical Engineering
Soggetto topico Electric power distribution
Electric power production
Energy storage
Renewable energy sources
Energy Grids and Networks
Electrical Power Engineering
Mechanical and Thermal Energy Storage
Renewable Energy
ISBN 9789819619658
9819619653
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Optimal Control of Large Number of Air Conditioners Driven by Dynamic Electric Carbon Emission Factor -- Application of a Bi-Level Optimization Model for Energy Storage Capacity Allocation in Distribution Network with Renewable Energy Integration -- Power Load Disaggregation Method based on Sparse Constraint -- Optimal Primary Frequency Support Demand Dispatch for Multiple Wind Turbines Considering Loss of Captured Wind Energy.
Record Nr. UNINA-9910984583903321
Wen Fushuan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui