Battery System Modeling
- Length: 354 pages
- Edition: 1
- Language: English
- Publisher: Elsevier
- Publication Date: 2021-07-09
- ISBN-10: 0323904726
- ISBN-13: 9780323904728
- Sales Rank: #0 (See Top 100 Books)
Battery System Modeling provides advances on the modeling of lithium-ion batteries. Offering step-by-step explanations, the book systematically guides the reader through the modeling of state of charge estimation, energy prediction, power evaluation, health estimation, and active control strategies. Using applications alongside practical case studies, each chapter shows the reader how to use the modeling tools provided. Moreover, the chemistry and characteristics are described in detail, with algorithms provided in every chapter. Providing a technical reference on the design and application of Li-ion battery management systems, this book is an ideal reference for researchers involved in batteries and energy storage.
Moreover, the step-by-step guidance and comprehensive introduction to the topic makes it accessible to audiences of all levels, from experienced engineers to graduates.
Front Matter Copyright Lithium-ion battery characteristics and applications Introduction to lithium-ion battery technology Development history Energy storage technologies Battery working mechanism Characteristic analysis Components and working principle Lithium-ion battery construction Charge-discharge strategies Lithium-ion battery chemistries Lithium-ion battery family Battery with different materials Solid-state lithium-ion battery Comparative battery types analysis Lithium-ion battery characteristics Internal parameter relationship Capacity characteristics Open-circuit voltage Internal resistance characteristic Power capability variation Coulombic efficiency Battery aging behavior Aging mechanisms Calendar aging process Temperature effect on aging process Lithium-ion battery applications Applications System state estimation Battery safety protection Battery life guarantee Status and trends Conclusion Acknowledgments Conflict of interest References Electrical equivalent circuit modeling Modeling method overview Modeling types and concepts Comparative equivalent models Commercial circuit models Electrochemical model Equivalent circuit model Principle description Modeling steps Model selection Parameter identification Improved internal resistance modeling Theoretical resistance modeling Battery model establishment Internal resistance description Open-circuit voltage characteristics Thevenin modeling Construction of Thevenin model Charge-discharge characteristics State equation establishment Mathematical description High-order modeling Second-order circuit modeling Internal resistance description Splice equivalent modeling Parameter identification algorithms Identification overview Least-square functional fitting Forgetting factor correction Experimental analysis Exponential curve fitting Polynomial relationship description Identified parameter variation Pulse voltage tracking effect Modeling accuracy verification Conclusion Acknowledgments References Conflict of interest Electrochemical Nernst modeling Nernst modeling and improvement Model building process Parameter identification strategies State-space description Modeling realization Simulation modeling structure Characteristic description Testing procedure design Model parameter identification Pulse current test logic Parameter identification results Curve fitting analysis Simulation result analysis Experimental verification Characteristic testing Pulse-power characteristic test Varying condition voltage tracking Modeling result and verification Conclusion Acknowledgments Conflict of interest References Battery state estimation methods State parameter identification State-of-charge estimation State-of-energy prediction State-of-power evaluation State-of-health determination Remaining-useful-life prediction Battery state influencing factors Temperature influence Charge-discharge current rate Self-discharging description Aging degree variation Traditional state estimation methods Algorithm comparison Foundational methods Kalman filtering extension Particle filtering estimation Machine learning algorithms State of art introduction Support vector machine Self-learning neural network Deep learning for life prediction Conclusion Acknowledgments Conflict of interest References Battery state-of-charge estimation methods Introduction State-of-charge estimation methods Calculation algorithm comparison Coordinate transformation Binary iterative algorithm Extended Kalman filtering Algorithm implementation Unscented kalman filtering Cubature Kalman filtering Iterative calculation and modeling Equivalent circuit modeling Parameter identification Kalman filtering algorithm Extended Taylor series expansion Estimation model construction Iterative prediction and correction Experimental result analysis Pulse-power characteristic test Estimation features and comparison Thermal influencing effect Time-varying condition influence Complex current rate verification Conclusion Acknowledgments Conflict of interest References Battery state-of-energy prediction methods Overview Iterative algorithm and realization Equivalent modeling Mathematical description Iterative calculation procedure Parameter initialization strategy Estimation model construction Improved prediction and correction Cholesky decomposition Calculation algorithm flow Covariance matching Improved correction strategy Experimental results analysis Parameter identification Pulse-power characteristic test Cyclic intermittent discharge Packing pulse current test Estimation processing effect Conclusion Acknowledgments Conflict of interest References Battery state-of-power evaluation methods State-space model construction State estimation structural design Algorithm overview Iterative calculation Calculation procedure design Computing framework design Iterative calculation steps Algorithm improvement Estimation modeling realization Experimental analysis Parameter identification results State estimating and voltage tracking Power-temperature variation Main charge-discharge condition test Pulse-current charge-discharge test Conclusion Acknowledgments Conflict of interest References Battery state-of-health estimation methods Equivalent modeling and description Equivalent circuit analysis Mathematical state-space expression Particle filtering algorithm Bayesian estimation Monte Carlo treatment Importance sampling Estimation modeling process Equivalent circuit modeling Calculation process design Particle degradation expression Resampling treatment Whole life-cycle experiments Experimental procedure design Capacity variation for new batteries Characteristic test for new batteries Aging test for pulse-current cycles Capacity variation for aged batteries Characteristics test for aged batteries Conclusion Acknowledgments Conflict of interest References Battery system active control strategies Overview of battery management systems Research status Classification and function Control system design Charging strategies for capacity extension Constant-current constant-voltage Multistage constant current Pulse current charging Sinusoidal ripple current Experimental analysis Balancing control methods Inconsistency mechanism State-of-balance description Balance strategy classification Passive equalization Active balancing management Temperature adjustment Overview of thermal controlling Air system circulation control Liquid cooling and heating Phase-change heat transfer Heat pipe temperature control Heatable thermal management Thermal model System construction and safety control Overall structure design Core factor measurement System protection Electrical interface connection Experimental performance test Conclusion Acknowledgments Conflict of interest References Index
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