Robust Battery Management System Design with MATLAB® /:
This book introduces several battery management problems and provides solutions using model-based approaches. It provides detailed coverage of battery management problems, including battery impedance estimation, battery capacity estimation, state of charge estimation, state of health estimation, bat...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Norwood, MA :
Artech House,
[2023]
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| Zusammenfassung: | This book introduces several battery management problems and provides solutions using model-based approaches. It provides detailed coverage of battery management problems, including battery impedance estimation, battery capacity estimation, state of charge estimation, state of health estimation, battery thermal management, and optimal charging algorithms. The book introduces important battery management problems in a modularized fashion, decoupling each battery management problem from others as much as possible, allowing you to focus on understanding a particular topic rather than having to understand all aspects of a battery management system. You will get the necessary background to understand, implement and improve battery fuel gauges in electric vehicles, and general state of health of the battery; use proven models and algorithms to estimate the thermal properties of a battery; and know the basics of smart battery charger design. You will also be equipped to accurately estimate battery features of vehicles, such as state of charge, expected charging time, and state of health, to make customized charging waveforms for each vehicle. The book teaches you how to create simulation environments to test and validate algorithms against model uncertainty and measurement noise. In addition, the importance of benchmarking battery management algorithms is covered, and several bench marking metrics are presented. Included MATLAB codes give you an easy way to test the algorithms using realistic data and to develop and test alternative solutions. This is a useful and timely guide for battery engineers at all levels, as well as research scientists and advanced students working in this robust and rapidly advancing area. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781630819538 1630819530 |
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| 245 | 1 | 0 | |a Robust Battery Management System Design with MATLAB® / |c Balakumar Balasingam. |
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| 588 | |a Description based on online resource; title from digital title page (viewed on October 10, 2023). | ||
| 520 | |a This book introduces several battery management problems and provides solutions using model-based approaches. It provides detailed coverage of battery management problems, including battery impedance estimation, battery capacity estimation, state of charge estimation, state of health estimation, battery thermal management, and optimal charging algorithms. The book introduces important battery management problems in a modularized fashion, decoupling each battery management problem from others as much as possible, allowing you to focus on understanding a particular topic rather than having to understand all aspects of a battery management system. You will get the necessary background to understand, implement and improve battery fuel gauges in electric vehicles, and general state of health of the battery; use proven models and algorithms to estimate the thermal properties of a battery; and know the basics of smart battery charger design. You will also be equipped to accurately estimate battery features of vehicles, such as state of charge, expected charging time, and state of health, to make customized charging waveforms for each vehicle. The book teaches you how to create simulation environments to test and validate algorithms against model uncertainty and measurement noise. In addition, the importance of benchmarking battery management algorithms is covered, and several bench marking metrics are presented. Included MATLAB codes give you an easy way to test the algorithms using realistic data and to develop and test alternative solutions. This is a useful and timely guide for battery engineers at all levels, as well as research scientists and advanced students working in this robust and rapidly advancing area. | ||
| 505 | 0 | |a Robust Battery ManagementSystem Design with MATLAB® -- Contents -- Preface -- Chapter 1 About This Book -- 1.1 Introduction -- 1.2 Who Is This Book For? -- 1.3 Use Cases -- 1.3.1 Remaining Mileage Estimation in an Electric Vehicle -- 1.3.2 Generating Battery Replacement Warning -- 1.3.3 Estimating the Expected Temperature Rise in a Battery Pack -- 1.3.4 Smart Battery Charger Design -- 1.3.5 EV Fleet Management -- 1.3.6 Teaching a Graduate-Level Course on BMS -- 1.4 What Is Novel in This Book? -- 1.4.1 Modularized Approach -- 1.4.2 Illustration of Algorithms Through Matlab Simulation -- 1.4.3 Emphasis on Both Theoretical and Practical Aspects -- 1.5 Organization of This Book -- 1.6 Matlab Codes -- 1.7 Bibliographical Notes -- References -- Chapter 2 Review of Required Mathematics -- 2.1 Introduction -- 2.2 Least Squares Estimator -- 2.3 Kalman Filter -- 2.4 Extended Kalman Filter -- 2.4.1 Assumptions of the EKF -- 2.5 Conclusions -- 2.6 Bibliographical Notes -- 2.7 Problems -- References -- Chapter 3 Battery Modeling -- 3.1 Introduction -- 3.2 Elements of Electrical Equivalent Circuit Models -- 3.2.1 DC Equivalent Circuit Model -- 3.2.2 AC Equivalent Circuit Model -- 3.3 Reduced-Order Models -- 3.3.1 Ideal Battery Model -- 3.3.2 Open-Circuit Voltage Model -- 3.3.3 Relaxation Model -- 3.3.4 Hysteresis Model -- 3.3.5 Enhanced Self-Correcting Model -- 3.3.6 R-int Model -- 3.3.7 Other Reduced-Order Models -- 3.4 Battery Power -- 3.5 Battery Capacity -- 3.5.1 Total Capacity -- 3.5.2 Discharge Capacity -- 3.5.3 Rated Capacity -- 3.5.4 Custom-Defined Capacity -- 3.6 State of Health -- 3.7 Battery Packs -- 3.8 Battery Simulator -- 3.9 Summary -- 3.10 Bibliographical Notes -- References -- Chapter 4 Open-Circuit Voltage Characterization -- 4.1 Introduction -- 4.2 Empirical OCV-SOC Models -- 4.2.1 Linear Regression Models. | |
| 505 | 8 | |a 4.2.2 Nonlinear Regression Models -- 4.2.3 Hybrid or Piecewise Linear Models -- 4.2.4 Tabular Model -- 4.3 OCV-SOC Model Parameter Estimation -- 4.3.1 Linear Least-Squares -- 4.3.2 Nonlinear Least-Squares -- 4.3.3 Hybrid Estimation -- 4.3.4 Tabular Model Estimation -- 4.4 Model Selection Metrics -- 4.4.1 OCV Prediction Error -- 4.4.2 Model Evaluation Metrics -- 4.4.3 Computational Complexity -- 4.4.4 Numerical Stability -- 4.4.5 System Requirement -- 4.5 Selection of the OCV-SOC Model -- 4.6 Summary -- 4.7 Bibliographical Notes -- References -- Chapter 5 Frequency-Domain Approaches to Battery ECM Identification -- 5.1 Introduction -- 5.2 Frequency Response of a Battery -- 5.3 Computing Frequency Response Using DFT -- 5.4 ECM Parameter Estimation Problem -- 5.5 Approximate Estimation of ECM Parameters -- 5.6 Causes of Parameter Estimation Error -- 5.6.1 Effect of Approximation -- 5.6.2 Effect of Measurement Noise -- 5.7 Improved Approach for Parameter Estimation -- 5.7.1 Estimation of the Warburg Coefficient -- 5.7.2 Estimation of the CT Components -- 5.7.3 Estimation of the SEI Components -- 5.7.4 Estimation of Resistance and Inductance -- 5.7.5 Feature Point Extraction -- 5.8 Demonstration -- 5.8.1 Demonstration Using Simulated Data -- 5.8.2 Demonstration Using Real Data -- 5.9 Summary -- 5.10 Bibliographical Notes -- References -- Chapter 6 Time-Domain Approaches to Battery ECM Identification -- 6.1 Introduction -- 6.2 Signal Model of a Battery -- 6.3 ECM Identification of Different Model Orders -- 6.4 Parameter Estimation Method -- 6.5 Performance Analysis -- 6.6 Simulation Analysis -- 6.6.1 Perfect ECM Assumption -- 6.6.2 Realistic ECM Assumption -- 6.6.3 Real Data -- 6.7 Summary -- 6.8 Bibliographical Notes -- References -- Chapter 7 Battery Capacity Estimation -- 7.1 Introduction -- 7.2 Basics of Battery Capacity Estimation. | |
| 505 | 8 | |a 7.2.1 Offline Estimation of Battery Capacity -- 7.2.2 Real-Time Capacity Estimation -- 7.3 Capacity Estimation in the Presence of Noise -- 7.3.1 LS Estimate -- 7.3.2 TLS Estimate -- 7.4 Recursive Estimates -- 7.4.1 Recursive LS -- 7.4.2 Recursive TLS -- 7.4.3 KF-Based Fusion -- 7.5 Experimental Results -- 7.5.1 OCV-SOC Characterization Test -- 7.5.2 Dynamic Discharge-Charge Profile -- 7.5.3 Real-Time Capacity Estimation -- 7.6 Conclusions -- 7.7 Bibliographical Notes -- References -- Chapter 8 Battery Fuel Gauging -- 8.1 Introduction -- 8.1.1 State of Charge -- 8.1.2 Time to Shut Down -- 8.1.3 State of Health -- 8.1.4 Remaining Useful Life -- 8.2 SOC Estimation: Coulomb Counting Approach -- 8.3 SOC Estimation: An OCV-Based Approach -- 8.4 SOC Estimation: Fusion Approach -- 8.4.1 Measurement Model -- 8.4.2 Scaling -- 8.4.3 Extended Kalman Filter for SOC Tracking -- 8.5 Filter Consistency Testing Approaches -- 8.5.1 Normalized Innovation Squared -- 8.5.2 Zero-Mean Test of Innovations -- 8.6 Results -- 8.7 Conclusions -- 8.8 Bibliographical Notes -- References -- Chapter 9 Battery Thermal Management -- 9.1 Introduction -- 9.2 Thermal Management Mediums -- 9.2.1 Air -- 9.2.2 Liquid -- 9.2.3 Phase Change Material -- 9.3 Battery Thermal Modeling -- 9.4 Simulation Results -- 9.5 Conclusions -- 9.6 Bibliographical Notes -- References -- Chapter 10 Optimal Charging Algorithms -- 10.1 Introduction -- 10.2 Charging Strategies -- 10.2.1 Constant Current Charging -- 10.2.2 Constant Voltage Charging -- 10.2.3 Constant Current-Constant Voltage Charging -- 10.2.4 Multistage Constant Current Charging -- 10.2.5 Pulse Charging -- 10.2.6 Trickle Charging -- 10.2.7 Float Charging -- 10.3 Optimized Charging Strategies -- 10.4 Numerical Results -- 10.5 Summary -- 10.6 Bibliographical Notes -- References. | |
| 505 | 8 | |a Chapter 11 Evaluation and Benchmarking of Battery Management Systems -- 11.1 Introduction -- 11.2 Coulomb Counting Metric -- 11.3 OCV-SOC Metric -- 11.4 TTV Metric -- 11.5 Demonstration of the BFG Evaluation -- 11.6 Summary -- 11.7 Bibliographical Notes -- References -- Appendix A: Closed-Form Derivation of the TLS Estimate -- Appendix B: Formal Derivation of Capacity -- B.1 Transformation of the Inverse Estimates -- B.2 The Expected Value of y -- B.3 The Variance of the Expected Value of y -- References -- Appendix C: Discretization of the State-Space Model -- List of Acronyms -- About the Author -- Index. | |
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| contents | Robust Battery ManagementSystem Design with MATLAB® -- Contents -- Preface -- Chapter 1 About This Book -- 1.1 Introduction -- 1.2 Who Is This Book For? -- 1.3 Use Cases -- 1.3.1 Remaining Mileage Estimation in an Electric Vehicle -- 1.3.2 Generating Battery Replacement Warning -- 1.3.3 Estimating the Expected Temperature Rise in a Battery Pack -- 1.3.4 Smart Battery Charger Design -- 1.3.5 EV Fleet Management -- 1.3.6 Teaching a Graduate-Level Course on BMS -- 1.4 What Is Novel in This Book? -- 1.4.1 Modularized Approach -- 1.4.2 Illustration of Algorithms Through Matlab Simulation -- 1.4.3 Emphasis on Both Theoretical and Practical Aspects -- 1.5 Organization of This Book -- 1.6 Matlab Codes -- 1.7 Bibliographical Notes -- References -- Chapter 2 Review of Required Mathematics -- 2.1 Introduction -- 2.2 Least Squares Estimator -- 2.3 Kalman Filter -- 2.4 Extended Kalman Filter -- 2.4.1 Assumptions of the EKF -- 2.5 Conclusions -- 2.6 Bibliographical Notes -- 2.7 Problems -- References -- Chapter 3 Battery Modeling -- 3.1 Introduction -- 3.2 Elements of Electrical Equivalent Circuit Models -- 3.2.1 DC Equivalent Circuit Model -- 3.2.2 AC Equivalent Circuit Model -- 3.3 Reduced-Order Models -- 3.3.1 Ideal Battery Model -- 3.3.2 Open-Circuit Voltage Model -- 3.3.3 Relaxation Model -- 3.3.4 Hysteresis Model -- 3.3.5 Enhanced Self-Correcting Model -- 3.3.6 R-int Model -- 3.3.7 Other Reduced-Order Models -- 3.4 Battery Power -- 3.5 Battery Capacity -- 3.5.1 Total Capacity -- 3.5.2 Discharge Capacity -- 3.5.3 Rated Capacity -- 3.5.4 Custom-Defined Capacity -- 3.6 State of Health -- 3.7 Battery Packs -- 3.8 Battery Simulator -- 3.9 Summary -- 3.10 Bibliographical Notes -- References -- Chapter 4 Open-Circuit Voltage Characterization -- 4.1 Introduction -- 4.2 Empirical OCV-SOC Models -- 4.2.1 Linear Regression Models. 4.2.2 Nonlinear Regression Models -- 4.2.3 Hybrid or Piecewise Linear Models -- 4.2.4 Tabular Model -- 4.3 OCV-SOC Model Parameter Estimation -- 4.3.1 Linear Least-Squares -- 4.3.2 Nonlinear Least-Squares -- 4.3.3 Hybrid Estimation -- 4.3.4 Tabular Model Estimation -- 4.4 Model Selection Metrics -- 4.4.1 OCV Prediction Error -- 4.4.2 Model Evaluation Metrics -- 4.4.3 Computational Complexity -- 4.4.4 Numerical Stability -- 4.4.5 System Requirement -- 4.5 Selection of the OCV-SOC Model -- 4.6 Summary -- 4.7 Bibliographical Notes -- References -- Chapter 5 Frequency-Domain Approaches to Battery ECM Identification -- 5.1 Introduction -- 5.2 Frequency Response of a Battery -- 5.3 Computing Frequency Response Using DFT -- 5.4 ECM Parameter Estimation Problem -- 5.5 Approximate Estimation of ECM Parameters -- 5.6 Causes of Parameter Estimation Error -- 5.6.1 Effect of Approximation -- 5.6.2 Effect of Measurement Noise -- 5.7 Improved Approach for Parameter Estimation -- 5.7.1 Estimation of the Warburg Coefficient -- 5.7.2 Estimation of the CT Components -- 5.7.3 Estimation of the SEI Components -- 5.7.4 Estimation of Resistance and Inductance -- 5.7.5 Feature Point Extraction -- 5.8 Demonstration -- 5.8.1 Demonstration Using Simulated Data -- 5.8.2 Demonstration Using Real Data -- 5.9 Summary -- 5.10 Bibliographical Notes -- References -- Chapter 6 Time-Domain Approaches to Battery ECM Identification -- 6.1 Introduction -- 6.2 Signal Model of a Battery -- 6.3 ECM Identification of Different Model Orders -- 6.4 Parameter Estimation Method -- 6.5 Performance Analysis -- 6.6 Simulation Analysis -- 6.6.1 Perfect ECM Assumption -- 6.6.2 Realistic ECM Assumption -- 6.6.3 Real Data -- 6.7 Summary -- 6.8 Bibliographical Notes -- References -- Chapter 7 Battery Capacity Estimation -- 7.1 Introduction -- 7.2 Basics of Battery Capacity Estimation. 7.2.1 Offline Estimation of Battery Capacity -- 7.2.2 Real-Time Capacity Estimation -- 7.3 Capacity Estimation in the Presence of Noise -- 7.3.1 LS Estimate -- 7.3.2 TLS Estimate -- 7.4 Recursive Estimates -- 7.4.1 Recursive LS -- 7.4.2 Recursive TLS -- 7.4.3 KF-Based Fusion -- 7.5 Experimental Results -- 7.5.1 OCV-SOC Characterization Test -- 7.5.2 Dynamic Discharge-Charge Profile -- 7.5.3 Real-Time Capacity Estimation -- 7.6 Conclusions -- 7.7 Bibliographical Notes -- References -- Chapter 8 Battery Fuel Gauging -- 8.1 Introduction -- 8.1.1 State of Charge -- 8.1.2 Time to Shut Down -- 8.1.3 State of Health -- 8.1.4 Remaining Useful Life -- 8.2 SOC Estimation: Coulomb Counting Approach -- 8.3 SOC Estimation: An OCV-Based Approach -- 8.4 SOC Estimation: Fusion Approach -- 8.4.1 Measurement Model -- 8.4.2 Scaling -- 8.4.3 Extended Kalman Filter for SOC Tracking -- 8.5 Filter Consistency Testing Approaches -- 8.5.1 Normalized Innovation Squared -- 8.5.2 Zero-Mean Test of Innovations -- 8.6 Results -- 8.7 Conclusions -- 8.8 Bibliographical Notes -- References -- Chapter 9 Battery Thermal Management -- 9.1 Introduction -- 9.2 Thermal Management Mediums -- 9.2.1 Air -- 9.2.2 Liquid -- 9.2.3 Phase Change Material -- 9.3 Battery Thermal Modeling -- 9.4 Simulation Results -- 9.5 Conclusions -- 9.6 Bibliographical Notes -- References -- Chapter 10 Optimal Charging Algorithms -- 10.1 Introduction -- 10.2 Charging Strategies -- 10.2.1 Constant Current Charging -- 10.2.2 Constant Voltage Charging -- 10.2.3 Constant Current-Constant Voltage Charging -- 10.2.4 Multistage Constant Current Charging -- 10.2.5 Pulse Charging -- 10.2.6 Trickle Charging -- 10.2.7 Float Charging -- 10.3 Optimized Charging Strategies -- 10.4 Numerical Results -- 10.5 Summary -- 10.6 Bibliographical Notes -- References. Chapter 11 Evaluation and Benchmarking of Battery Management Systems -- 11.1 Introduction -- 11.2 Coulomb Counting Metric -- 11.3 OCV-SOC Metric -- 11.4 TTV Metric -- 11.5 Demonstration of the BFG Evaluation -- 11.6 Summary -- 11.7 Bibliographical Notes -- References -- Appendix A: Closed-Form Derivation of the TLS Estimate -- Appendix B: Formal Derivation of Capacity -- B.1 Transformation of the Inverse Estimates -- B.2 The Expected Value of y -- B.3 The Variance of the Expected Value of y -- References -- Appendix C: Discretization of the State-Space Model -- List of Acronyms -- About the Author -- Index. |
| ctrlnum | (OCoLC)1396551082 |
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You will also be equipped to accurately estimate battery features of vehicles, such as state of charge, expected charging time, and state of health, to make customized charging waveforms for each vehicle. The book teaches you how to create simulation environments to test and validate algorithms against model uncertainty and measurement noise. In addition, the importance of benchmarking battery management algorithms is covered, and several bench marking metrics are presented. Included MATLAB codes give you an easy way to test the algorithms using realistic data and to develop and test alternative solutions. This is a useful and timely guide for battery engineers at all levels, as well as research scientists and advanced students working in this robust and rapidly advancing area.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Robust Battery ManagementSystem Design with MATLAB® -- Contents -- Preface -- Chapter 1 About This Book -- 1.1 Introduction -- 1.2 Who Is This Book For? -- 1.3 Use Cases -- 1.3.1 Remaining Mileage Estimation in an Electric Vehicle -- 1.3.2 Generating Battery Replacement Warning -- 1.3.3 Estimating the Expected Temperature Rise in a Battery Pack -- 1.3.4 Smart Battery Charger Design -- 1.3.5 EV Fleet Management -- 1.3.6 Teaching a Graduate-Level Course on BMS -- 1.4 What Is Novel in This Book? -- 1.4.1 Modularized Approach -- 1.4.2 Illustration of Algorithms Through Matlab Simulation -- 1.4.3 Emphasis on Both Theoretical and Practical Aspects -- 1.5 Organization of This Book -- 1.6 Matlab Codes -- 1.7 Bibliographical Notes -- References -- Chapter 2 Review of Required Mathematics -- 2.1 Introduction -- 2.2 Least Squares Estimator -- 2.3 Kalman Filter -- 2.4 Extended Kalman Filter -- 2.4.1 Assumptions of the EKF -- 2.5 Conclusions -- 2.6 Bibliographical Notes -- 2.7 Problems -- References -- Chapter 3 Battery Modeling -- 3.1 Introduction -- 3.2 Elements of Electrical Equivalent Circuit Models -- 3.2.1 DC Equivalent Circuit Model -- 3.2.2 AC Equivalent Circuit Model -- 3.3 Reduced-Order Models -- 3.3.1 Ideal Battery Model -- 3.3.2 Open-Circuit Voltage Model -- 3.3.3 Relaxation Model -- 3.3.4 Hysteresis Model -- 3.3.5 Enhanced Self-Correcting Model -- 3.3.6 R-int Model -- 3.3.7 Other Reduced-Order Models -- 3.4 Battery Power -- 3.5 Battery Capacity -- 3.5.1 Total Capacity -- 3.5.2 Discharge Capacity -- 3.5.3 Rated Capacity -- 3.5.4 Custom-Defined Capacity -- 3.6 State of Health -- 3.7 Battery Packs -- 3.8 Battery Simulator -- 3.9 Summary -- 3.10 Bibliographical Notes -- References -- Chapter 4 Open-Circuit Voltage Characterization -- 4.1 Introduction -- 4.2 Empirical OCV-SOC Models -- 4.2.1 Linear Regression Models.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.2.2 Nonlinear Regression Models -- 4.2.3 Hybrid or Piecewise Linear Models -- 4.2.4 Tabular Model -- 4.3 OCV-SOC Model Parameter Estimation -- 4.3.1 Linear Least-Squares -- 4.3.2 Nonlinear Least-Squares -- 4.3.3 Hybrid Estimation -- 4.3.4 Tabular Model Estimation -- 4.4 Model Selection Metrics -- 4.4.1 OCV Prediction Error -- 4.4.2 Model Evaluation Metrics -- 4.4.3 Computational Complexity -- 4.4.4 Numerical Stability -- 4.4.5 System Requirement -- 4.5 Selection of the OCV-SOC Model -- 4.6 Summary -- 4.7 Bibliographical Notes -- References -- Chapter 5 Frequency-Domain Approaches to Battery ECM Identification -- 5.1 Introduction -- 5.2 Frequency Response of a Battery -- 5.3 Computing Frequency Response Using DFT -- 5.4 ECM Parameter Estimation Problem -- 5.5 Approximate Estimation of ECM Parameters -- 5.6 Causes of Parameter Estimation Error -- 5.6.1 Effect of Approximation -- 5.6.2 Effect of Measurement Noise -- 5.7 Improved Approach for Parameter Estimation -- 5.7.1 Estimation of the Warburg Coefficient -- 5.7.2 Estimation of the CT Components -- 5.7.3 Estimation of the SEI Components -- 5.7.4 Estimation of Resistance and Inductance -- 5.7.5 Feature Point Extraction -- 5.8 Demonstration -- 5.8.1 Demonstration Using Simulated Data -- 5.8.2 Demonstration Using Real Data -- 5.9 Summary -- 5.10 Bibliographical Notes -- References -- Chapter 6 Time-Domain Approaches to Battery ECM Identification -- 6.1 Introduction -- 6.2 Signal Model of a Battery -- 6.3 ECM Identification of Different Model Orders -- 6.4 Parameter Estimation Method -- 6.5 Performance Analysis -- 6.6 Simulation Analysis -- 6.6.1 Perfect ECM Assumption -- 6.6.2 Realistic ECM Assumption -- 6.6.3 Real Data -- 6.7 Summary -- 6.8 Bibliographical Notes -- References -- Chapter 7 Battery Capacity Estimation -- 7.1 Introduction -- 7.2 Basics of Battery Capacity Estimation.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7.2.1 Offline Estimation of Battery Capacity -- 7.2.2 Real-Time Capacity Estimation -- 7.3 Capacity Estimation in the Presence of Noise -- 7.3.1 LS Estimate -- 7.3.2 TLS Estimate -- 7.4 Recursive Estimates -- 7.4.1 Recursive LS -- 7.4.2 Recursive TLS -- 7.4.3 KF-Based Fusion -- 7.5 Experimental Results -- 7.5.1 OCV-SOC Characterization Test -- 7.5.2 Dynamic Discharge-Charge Profile -- 7.5.3 Real-Time Capacity Estimation -- 7.6 Conclusions -- 7.7 Bibliographical Notes -- References -- Chapter 8 Battery Fuel Gauging -- 8.1 Introduction -- 8.1.1 State of Charge -- 8.1.2 Time to Shut Down -- 8.1.3 State of Health -- 8.1.4 Remaining Useful Life -- 8.2 SOC Estimation: Coulomb Counting Approach -- 8.3 SOC Estimation: An OCV-Based Approach -- 8.4 SOC Estimation: Fusion Approach -- 8.4.1 Measurement Model -- 8.4.2 Scaling -- 8.4.3 Extended Kalman Filter for SOC Tracking -- 8.5 Filter Consistency Testing Approaches -- 8.5.1 Normalized Innovation Squared -- 8.5.2 Zero-Mean Test of Innovations -- 8.6 Results -- 8.7 Conclusions -- 8.8 Bibliographical Notes -- References -- Chapter 9 Battery Thermal Management -- 9.1 Introduction -- 9.2 Thermal Management Mediums -- 9.2.1 Air -- 9.2.2 Liquid -- 9.2.3 Phase Change Material -- 9.3 Battery Thermal Modeling -- 9.4 Simulation Results -- 9.5 Conclusions -- 9.6 Bibliographical Notes -- References -- Chapter 10 Optimal Charging Algorithms -- 10.1 Introduction -- 10.2 Charging Strategies -- 10.2.1 Constant Current Charging -- 10.2.2 Constant Voltage Charging -- 10.2.3 Constant Current-Constant Voltage Charging -- 10.2.4 Multistage Constant Current Charging -- 10.2.5 Pulse Charging -- 10.2.6 Trickle Charging -- 10.2.7 Float Charging -- 10.3 Optimized Charging Strategies -- 10.4 Numerical Results -- 10.5 Summary -- 10.6 Bibliographical Notes -- References.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Chapter 11 Evaluation and Benchmarking of Battery Management Systems -- 11.1 Introduction -- 11.2 Coulomb Counting Metric -- 11.3 OCV-SOC Metric -- 11.4 TTV Metric -- 11.5 Demonstration of the BFG Evaluation -- 11.6 Summary -- 11.7 Bibliographical Notes -- References -- Appendix A: Closed-Form Derivation of the TLS Estimate -- Appendix B: Formal Derivation of Capacity -- B.1 Transformation of the Inverse Estimates -- B.2 The Expected Value of y -- B.3 The Variance of the Expected Value of y -- References -- Appendix C: Discretization of the State-Space Model -- List of Acronyms -- About the Author -- Index.</subfield></datafield><datafield tag="630" ind1="0" ind2="0"><subfield code="a">MATLAB.</subfield><subfield code="0">http://id.loc.gov/authorities/names/n92036881</subfield></datafield><datafield tag="630" ind1="0" ind2="7"><subfield code="a">MATLAB.</subfield><subfield code="2">fast</subfield><subfield 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| id | ZDB-4-EBA-on1396551082 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:43Z |
| institution | BVB |
| isbn | 9781630819538 1630819530 |
| language | English |
| oclc_num | 1396551082 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Artech House, |
| record_format | marc |
| spelling | Balasingam, Balakumar. Robust Battery Management System Design with MATLAB® / Balakumar Balasingam. Norwood, MA : Artech House, [2023] 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Description based on online resource; title from digital title page (viewed on October 10, 2023). This book introduces several battery management problems and provides solutions using model-based approaches. It provides detailed coverage of battery management problems, including battery impedance estimation, battery capacity estimation, state of charge estimation, state of health estimation, battery thermal management, and optimal charging algorithms. The book introduces important battery management problems in a modularized fashion, decoupling each battery management problem from others as much as possible, allowing you to focus on understanding a particular topic rather than having to understand all aspects of a battery management system. You will get the necessary background to understand, implement and improve battery fuel gauges in electric vehicles, and general state of health of the battery; use proven models and algorithms to estimate the thermal properties of a battery; and know the basics of smart battery charger design. You will also be equipped to accurately estimate battery features of vehicles, such as state of charge, expected charging time, and state of health, to make customized charging waveforms for each vehicle. The book teaches you how to create simulation environments to test and validate algorithms against model uncertainty and measurement noise. In addition, the importance of benchmarking battery management algorithms is covered, and several bench marking metrics are presented. Included MATLAB codes give you an easy way to test the algorithms using realistic data and to develop and test alternative solutions. This is a useful and timely guide for battery engineers at all levels, as well as research scientists and advanced students working in this robust and rapidly advancing area. Robust Battery ManagementSystem Design with MATLAB® -- Contents -- Preface -- Chapter 1 About This Book -- 1.1 Introduction -- 1.2 Who Is This Book For? -- 1.3 Use Cases -- 1.3.1 Remaining Mileage Estimation in an Electric Vehicle -- 1.3.2 Generating Battery Replacement Warning -- 1.3.3 Estimating the Expected Temperature Rise in a Battery Pack -- 1.3.4 Smart Battery Charger Design -- 1.3.5 EV Fleet Management -- 1.3.6 Teaching a Graduate-Level Course on BMS -- 1.4 What Is Novel in This Book? -- 1.4.1 Modularized Approach -- 1.4.2 Illustration of Algorithms Through Matlab Simulation -- 1.4.3 Emphasis on Both Theoretical and Practical Aspects -- 1.5 Organization of This Book -- 1.6 Matlab Codes -- 1.7 Bibliographical Notes -- References -- Chapter 2 Review of Required Mathematics -- 2.1 Introduction -- 2.2 Least Squares Estimator -- 2.3 Kalman Filter -- 2.4 Extended Kalman Filter -- 2.4.1 Assumptions of the EKF -- 2.5 Conclusions -- 2.6 Bibliographical Notes -- 2.7 Problems -- References -- Chapter 3 Battery Modeling -- 3.1 Introduction -- 3.2 Elements of Electrical Equivalent Circuit Models -- 3.2.1 DC Equivalent Circuit Model -- 3.2.2 AC Equivalent Circuit Model -- 3.3 Reduced-Order Models -- 3.3.1 Ideal Battery Model -- 3.3.2 Open-Circuit Voltage Model -- 3.3.3 Relaxation Model -- 3.3.4 Hysteresis Model -- 3.3.5 Enhanced Self-Correcting Model -- 3.3.6 R-int Model -- 3.3.7 Other Reduced-Order Models -- 3.4 Battery Power -- 3.5 Battery Capacity -- 3.5.1 Total Capacity -- 3.5.2 Discharge Capacity -- 3.5.3 Rated Capacity -- 3.5.4 Custom-Defined Capacity -- 3.6 State of Health -- 3.7 Battery Packs -- 3.8 Battery Simulator -- 3.9 Summary -- 3.10 Bibliographical Notes -- References -- Chapter 4 Open-Circuit Voltage Characterization -- 4.1 Introduction -- 4.2 Empirical OCV-SOC Models -- 4.2.1 Linear Regression Models. 4.2.2 Nonlinear Regression Models -- 4.2.3 Hybrid or Piecewise Linear Models -- 4.2.4 Tabular Model -- 4.3 OCV-SOC Model Parameter Estimation -- 4.3.1 Linear Least-Squares -- 4.3.2 Nonlinear Least-Squares -- 4.3.3 Hybrid Estimation -- 4.3.4 Tabular Model Estimation -- 4.4 Model Selection Metrics -- 4.4.1 OCV Prediction Error -- 4.4.2 Model Evaluation Metrics -- 4.4.3 Computational Complexity -- 4.4.4 Numerical Stability -- 4.4.5 System Requirement -- 4.5 Selection of the OCV-SOC Model -- 4.6 Summary -- 4.7 Bibliographical Notes -- References -- Chapter 5 Frequency-Domain Approaches to Battery ECM Identification -- 5.1 Introduction -- 5.2 Frequency Response of a Battery -- 5.3 Computing Frequency Response Using DFT -- 5.4 ECM Parameter Estimation Problem -- 5.5 Approximate Estimation of ECM Parameters -- 5.6 Causes of Parameter Estimation Error -- 5.6.1 Effect of Approximation -- 5.6.2 Effect of Measurement Noise -- 5.7 Improved Approach for Parameter Estimation -- 5.7.1 Estimation of the Warburg Coefficient -- 5.7.2 Estimation of the CT Components -- 5.7.3 Estimation of the SEI Components -- 5.7.4 Estimation of Resistance and Inductance -- 5.7.5 Feature Point Extraction -- 5.8 Demonstration -- 5.8.1 Demonstration Using Simulated Data -- 5.8.2 Demonstration Using Real Data -- 5.9 Summary -- 5.10 Bibliographical Notes -- References -- Chapter 6 Time-Domain Approaches to Battery ECM Identification -- 6.1 Introduction -- 6.2 Signal Model of a Battery -- 6.3 ECM Identification of Different Model Orders -- 6.4 Parameter Estimation Method -- 6.5 Performance Analysis -- 6.6 Simulation Analysis -- 6.6.1 Perfect ECM Assumption -- 6.6.2 Realistic ECM Assumption -- 6.6.3 Real Data -- 6.7 Summary -- 6.8 Bibliographical Notes -- References -- Chapter 7 Battery Capacity Estimation -- 7.1 Introduction -- 7.2 Basics of Battery Capacity Estimation. 7.2.1 Offline Estimation of Battery Capacity -- 7.2.2 Real-Time Capacity Estimation -- 7.3 Capacity Estimation in the Presence of Noise -- 7.3.1 LS Estimate -- 7.3.2 TLS Estimate -- 7.4 Recursive Estimates -- 7.4.1 Recursive LS -- 7.4.2 Recursive TLS -- 7.4.3 KF-Based Fusion -- 7.5 Experimental Results -- 7.5.1 OCV-SOC Characterization Test -- 7.5.2 Dynamic Discharge-Charge Profile -- 7.5.3 Real-Time Capacity Estimation -- 7.6 Conclusions -- 7.7 Bibliographical Notes -- References -- Chapter 8 Battery Fuel Gauging -- 8.1 Introduction -- 8.1.1 State of Charge -- 8.1.2 Time to Shut Down -- 8.1.3 State of Health -- 8.1.4 Remaining Useful Life -- 8.2 SOC Estimation: Coulomb Counting Approach -- 8.3 SOC Estimation: An OCV-Based Approach -- 8.4 SOC Estimation: Fusion Approach -- 8.4.1 Measurement Model -- 8.4.2 Scaling -- 8.4.3 Extended Kalman Filter for SOC Tracking -- 8.5 Filter Consistency Testing Approaches -- 8.5.1 Normalized Innovation Squared -- 8.5.2 Zero-Mean Test of Innovations -- 8.6 Results -- 8.7 Conclusions -- 8.8 Bibliographical Notes -- References -- Chapter 9 Battery Thermal Management -- 9.1 Introduction -- 9.2 Thermal Management Mediums -- 9.2.1 Air -- 9.2.2 Liquid -- 9.2.3 Phase Change Material -- 9.3 Battery Thermal Modeling -- 9.4 Simulation Results -- 9.5 Conclusions -- 9.6 Bibliographical Notes -- References -- Chapter 10 Optimal Charging Algorithms -- 10.1 Introduction -- 10.2 Charging Strategies -- 10.2.1 Constant Current Charging -- 10.2.2 Constant Voltage Charging -- 10.2.3 Constant Current-Constant Voltage Charging -- 10.2.4 Multistage Constant Current Charging -- 10.2.5 Pulse Charging -- 10.2.6 Trickle Charging -- 10.2.7 Float Charging -- 10.3 Optimized Charging Strategies -- 10.4 Numerical Results -- 10.5 Summary -- 10.6 Bibliographical Notes -- References. Chapter 11 Evaluation and Benchmarking of Battery Management Systems -- 11.1 Introduction -- 11.2 Coulomb Counting Metric -- 11.3 OCV-SOC Metric -- 11.4 TTV Metric -- 11.5 Demonstration of the BFG Evaluation -- 11.6 Summary -- 11.7 Bibliographical Notes -- References -- Appendix A: Closed-Form Derivation of the TLS Estimate -- Appendix B: Formal Derivation of Capacity -- B.1 Transformation of the Inverse Estimates -- B.2 The Expected Value of y -- B.3 The Variance of the Expected Value of y -- References -- Appendix C: Discretization of the State-Space Model -- List of Acronyms -- About the Author -- Index. MATLAB. http://id.loc.gov/authorities/names/n92036881 MATLAB. fast (OCoLC)fst01365096 Battery management systems Design. Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Commande robuste. Robust control. fast (OCoLC)fst01099109 Print version: 1630819522 9781630819521 (OCoLC)1375542258 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3675083 Volltext |
| spellingShingle | Balasingam, Balakumar Robust Battery Management System Design with MATLAB® / Robust Battery ManagementSystem Design with MATLAB® -- Contents -- Preface -- Chapter 1 About This Book -- 1.1 Introduction -- 1.2 Who Is This Book For? -- 1.3 Use Cases -- 1.3.1 Remaining Mileage Estimation in an Electric Vehicle -- 1.3.2 Generating Battery Replacement Warning -- 1.3.3 Estimating the Expected Temperature Rise in a Battery Pack -- 1.3.4 Smart Battery Charger Design -- 1.3.5 EV Fleet Management -- 1.3.6 Teaching a Graduate-Level Course on BMS -- 1.4 What Is Novel in This Book? -- 1.4.1 Modularized Approach -- 1.4.2 Illustration of Algorithms Through Matlab Simulation -- 1.4.3 Emphasis on Both Theoretical and Practical Aspects -- 1.5 Organization of This Book -- 1.6 Matlab Codes -- 1.7 Bibliographical Notes -- References -- Chapter 2 Review of Required Mathematics -- 2.1 Introduction -- 2.2 Least Squares Estimator -- 2.3 Kalman Filter -- 2.4 Extended Kalman Filter -- 2.4.1 Assumptions of the EKF -- 2.5 Conclusions -- 2.6 Bibliographical Notes -- 2.7 Problems -- References -- Chapter 3 Battery Modeling -- 3.1 Introduction -- 3.2 Elements of Electrical Equivalent Circuit Models -- 3.2.1 DC Equivalent Circuit Model -- 3.2.2 AC Equivalent Circuit Model -- 3.3 Reduced-Order Models -- 3.3.1 Ideal Battery Model -- 3.3.2 Open-Circuit Voltage Model -- 3.3.3 Relaxation Model -- 3.3.4 Hysteresis Model -- 3.3.5 Enhanced Self-Correcting Model -- 3.3.6 R-int Model -- 3.3.7 Other Reduced-Order Models -- 3.4 Battery Power -- 3.5 Battery Capacity -- 3.5.1 Total Capacity -- 3.5.2 Discharge Capacity -- 3.5.3 Rated Capacity -- 3.5.4 Custom-Defined Capacity -- 3.6 State of Health -- 3.7 Battery Packs -- 3.8 Battery Simulator -- 3.9 Summary -- 3.10 Bibliographical Notes -- References -- Chapter 4 Open-Circuit Voltage Characterization -- 4.1 Introduction -- 4.2 Empirical OCV-SOC Models -- 4.2.1 Linear Regression Models. 4.2.2 Nonlinear Regression Models -- 4.2.3 Hybrid or Piecewise Linear Models -- 4.2.4 Tabular Model -- 4.3 OCV-SOC Model Parameter Estimation -- 4.3.1 Linear Least-Squares -- 4.3.2 Nonlinear Least-Squares -- 4.3.3 Hybrid Estimation -- 4.3.4 Tabular Model Estimation -- 4.4 Model Selection Metrics -- 4.4.1 OCV Prediction Error -- 4.4.2 Model Evaluation Metrics -- 4.4.3 Computational Complexity -- 4.4.4 Numerical Stability -- 4.4.5 System Requirement -- 4.5 Selection of the OCV-SOC Model -- 4.6 Summary -- 4.7 Bibliographical Notes -- References -- Chapter 5 Frequency-Domain Approaches to Battery ECM Identification -- 5.1 Introduction -- 5.2 Frequency Response of a Battery -- 5.3 Computing Frequency Response Using DFT -- 5.4 ECM Parameter Estimation Problem -- 5.5 Approximate Estimation of ECM Parameters -- 5.6 Causes of Parameter Estimation Error -- 5.6.1 Effect of Approximation -- 5.6.2 Effect of Measurement Noise -- 5.7 Improved Approach for Parameter Estimation -- 5.7.1 Estimation of the Warburg Coefficient -- 5.7.2 Estimation of the CT Components -- 5.7.3 Estimation of the SEI Components -- 5.7.4 Estimation of Resistance and Inductance -- 5.7.5 Feature Point Extraction -- 5.8 Demonstration -- 5.8.1 Demonstration Using Simulated Data -- 5.8.2 Demonstration Using Real Data -- 5.9 Summary -- 5.10 Bibliographical Notes -- References -- Chapter 6 Time-Domain Approaches to Battery ECM Identification -- 6.1 Introduction -- 6.2 Signal Model of a Battery -- 6.3 ECM Identification of Different Model Orders -- 6.4 Parameter Estimation Method -- 6.5 Performance Analysis -- 6.6 Simulation Analysis -- 6.6.1 Perfect ECM Assumption -- 6.6.2 Realistic ECM Assumption -- 6.6.3 Real Data -- 6.7 Summary -- 6.8 Bibliographical Notes -- References -- Chapter 7 Battery Capacity Estimation -- 7.1 Introduction -- 7.2 Basics of Battery Capacity Estimation. 7.2.1 Offline Estimation of Battery Capacity -- 7.2.2 Real-Time Capacity Estimation -- 7.3 Capacity Estimation in the Presence of Noise -- 7.3.1 LS Estimate -- 7.3.2 TLS Estimate -- 7.4 Recursive Estimates -- 7.4.1 Recursive LS -- 7.4.2 Recursive TLS -- 7.4.3 KF-Based Fusion -- 7.5 Experimental Results -- 7.5.1 OCV-SOC Characterization Test -- 7.5.2 Dynamic Discharge-Charge Profile -- 7.5.3 Real-Time Capacity Estimation -- 7.6 Conclusions -- 7.7 Bibliographical Notes -- References -- Chapter 8 Battery Fuel Gauging -- 8.1 Introduction -- 8.1.1 State of Charge -- 8.1.2 Time to Shut Down -- 8.1.3 State of Health -- 8.1.4 Remaining Useful Life -- 8.2 SOC Estimation: Coulomb Counting Approach -- 8.3 SOC Estimation: An OCV-Based Approach -- 8.4 SOC Estimation: Fusion Approach -- 8.4.1 Measurement Model -- 8.4.2 Scaling -- 8.4.3 Extended Kalman Filter for SOC Tracking -- 8.5 Filter Consistency Testing Approaches -- 8.5.1 Normalized Innovation Squared -- 8.5.2 Zero-Mean Test of Innovations -- 8.6 Results -- 8.7 Conclusions -- 8.8 Bibliographical Notes -- References -- Chapter 9 Battery Thermal Management -- 9.1 Introduction -- 9.2 Thermal Management Mediums -- 9.2.1 Air -- 9.2.2 Liquid -- 9.2.3 Phase Change Material -- 9.3 Battery Thermal Modeling -- 9.4 Simulation Results -- 9.5 Conclusions -- 9.6 Bibliographical Notes -- References -- Chapter 10 Optimal Charging Algorithms -- 10.1 Introduction -- 10.2 Charging Strategies -- 10.2.1 Constant Current Charging -- 10.2.2 Constant Voltage Charging -- 10.2.3 Constant Current-Constant Voltage Charging -- 10.2.4 Multistage Constant Current Charging -- 10.2.5 Pulse Charging -- 10.2.6 Trickle Charging -- 10.2.7 Float Charging -- 10.3 Optimized Charging Strategies -- 10.4 Numerical Results -- 10.5 Summary -- 10.6 Bibliographical Notes -- References. Chapter 11 Evaluation and Benchmarking of Battery Management Systems -- 11.1 Introduction -- 11.2 Coulomb Counting Metric -- 11.3 OCV-SOC Metric -- 11.4 TTV Metric -- 11.5 Demonstration of the BFG Evaluation -- 11.6 Summary -- 11.7 Bibliographical Notes -- References -- Appendix A: Closed-Form Derivation of the TLS Estimate -- Appendix B: Formal Derivation of Capacity -- B.1 Transformation of the Inverse Estimates -- B.2 The Expected Value of y -- B.3 The Variance of the Expected Value of y -- References -- Appendix C: Discretization of the State-Space Model -- List of Acronyms -- About the Author -- Index. MATLAB. http://id.loc.gov/authorities/names/n92036881 MATLAB. fast (OCoLC)fst01365096 Battery management systems Design. Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Commande robuste. Robust control. fast (OCoLC)fst01099109 |
| subject_GND | http://id.loc.gov/authorities/names/n92036881 (OCoLC)fst01365096 http://id.loc.gov/authorities/subjects/sh99013330 (OCoLC)fst01099109 |
| title | Robust Battery Management System Design with MATLAB® / |
| title_auth | Robust Battery Management System Design with MATLAB® / |
| title_exact_search | Robust Battery Management System Design with MATLAB® / |
| title_full | Robust Battery Management System Design with MATLAB® / Balakumar Balasingam. |
| title_fullStr | Robust Battery Management System Design with MATLAB® / Balakumar Balasingam. |
| title_full_unstemmed | Robust Battery Management System Design with MATLAB® / Balakumar Balasingam. |
| title_short | Robust Battery Management System Design with MATLAB® / |
| title_sort | robust battery management system design with matlab® |
| topic | MATLAB. http://id.loc.gov/authorities/names/n92036881 MATLAB. fast (OCoLC)fst01365096 Battery management systems Design. Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Commande robuste. Robust control. fast (OCoLC)fst01099109 |
| topic_facet | MATLAB. Battery management systems Design. Robust control. Commande robuste. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3675083 |
| work_keys_str_mv | AT balasingambalakumar robustbatterymanagementsystemdesignwithmatlab |