Battery State Estimation And Control For Power Buffering Applications
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Battery State Estimation And Control For Power Buffering Applications
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Author : Herman L. N. Wiegman
language : en
Publisher:
Release Date : 2001
Battery State Estimation And Control For Power Buffering Applications written by Herman L. N. Wiegman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Electrical engineering categories.
Battery State Estimation And Control For Power Buffering Applications
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Author : Herman L. N. Wiegman
language : en
Publisher:
Release Date : 1999
Battery State Estimation And Control For Power Buffering Applications written by Herman L. N. Wiegman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
State Estimation Strategies In Lithium Ion Battery Management Systems
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Author : Shunli Wang
language : en
Publisher: Elsevier
Release Date : 2023-07-14
State Estimation Strategies In Lithium Ion Battery Management Systems written by Shunli Wang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-14 with Business & Economics categories.
State Estimation Strategies in Lithium-ion Battery Management Systems presents key technologies and methodologies in modeling and monitoring charge, energy, power and health of lithium-ion batteries. Sections introduce core state parameters of the lithium-ion battery, reviewing existing research and the significance of the prediction of core state parameters of the lithium-ion battery and analyzing the advantages and disadvantages of prediction methods of core state parameters. Characteristic analysis and aging characteristics are then discussed. Subsequent chapters elaborate, in detail, on modeling and parameter identification methods and advanced estimation techniques in different application scenarios. Offering a systematic approach supported by examples, process diagrams, flowcharts, algorithms, and other visual elements, this book is of interest to researchers, advanced students and scientists in energy storage, control, automation, electrical engineering, power systems, materials science and chemical engineering, as well as to engineers, R&D professionals, and other industry personnel. Introduces lithium-ion batteries, characteristics and core state parameters Examines battery equivalent modeling and provides advanced methods for battery state estimation Analyzes current technology and future opportunities
Multidimensional Lithium Ion Battery Status Monitoring
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Author : Shunli Wang
language : en
Publisher: CRC Press
Release Date : 2022-12-28
Multidimensional Lithium Ion Battery Status Monitoring written by Shunli Wang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-28 with Technology & Engineering categories.
Multidimensional Lithium-Ion Battery Status Monitoring focuses on equivalent circuit modeling, parameter identification, and state estimation in lithium-ion battery power applications. It explores the requirements of high-power lithium-ion batteries for new energy vehicles and systematically describes the key technologies in core state estimation based on battery equivalent modeling and parameter identification methods of lithium-ion batteries, providing a technical reference for the design and application of power lithium-ion battery management systems. Reviews Li-ion battery characteristics and applications. Covers battery equivalent modeling, including electrical circuit modeling and parameter identification theory Discusses battery state estimation methods, including state of charge estimation, state of energy prediction, state of power evaluation, state of health estimation, and cycle life estimation Introduces equivalent modeling and state estimation algorithms that can be applied to new energy measurement and control in large-scale energy storage Includes a large number of examples and case studies This book has been developed as a reference for researchers and advanced students in energy and electrical engineering.
Battery State Estimation
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Author : Shunli Wang
language : en
Publisher: IET
Release Date : 2021-12-02
Battery State Estimation written by Shunli Wang and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-02 with Technology & Engineering categories.
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.
Long Term Health State Estimation Of Energy Storage Lithium Ion Battery Packs
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Author : Qi Huang
language : en
Publisher: Springer Nature
Release Date : 2023-08-18
Long Term Health State Estimation Of Energy Storage Lithium Ion Battery Packs written by Qi Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-18 with Technology & Engineering categories.
This book investigates in detail long-term health state estimation technology of energy storage systems, assessing its potential use to replace common filtering methods that constructs by equivalent circuit model with a data-driven method combined with electrochemical modeling, which can reflect the battery internal characteristics, the battery degradation modes, and the battery pack health state. Studies on long-term health state estimation have attracted engineers and scientists from various disciplines, such as electrical engineering, materials, automation, energy, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of extraction for health indicators and the significant influence of electrochemical modeling and data-driven issues in the design and optimization of health state estimation in energy storage systems. The book is intended for undergraduate and graduate students who are interested in new energy measurement and control technology, researchers investigating energy storage systems, and structure/circuit design engineers working on energy storage cell and pack.
Modeling And Numerical Simulations I
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Author : Mordechay Schlesinger
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-25
Modeling And Numerical Simulations I written by Mordechay Schlesinger and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-25 with Science categories.
This volume is meant as an introductory resource aimed at practitioners of electrochemistry research, technology and development mainly at the atomic, molecular or macromolecular levels. Emphasis is placed at length scales in the 1-100 nm range. The aim of the volume is to help provide understanding of electrochemical phenomena and materials at the nanoscale through modeling and numeric simulations. It is also designed to serve as a means to create and use structures.
Computationally Efficient Online Model Based Control And Estimation For Lithium Ion Batteries
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Author : Ji Liu
language : en
Publisher:
Release Date : 2017
Computationally Efficient Online Model Based Control And Estimation For Lithium Ion Batteries written by Ji Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
This dissertation presents a framework for computationally-efficient, health-consciousonline state estimation and control in lithium-ion batteries. The framework buildson three main tools, namely, (i) battery model reformulation and (ii) pseudo-spectral optimization for (iii) differential flatness. All of these tools already existin the literature. However, their application to electrochemical battery estimationand control, both separately and in an integrated manner, represents a significantaddition to the literature. The dissertation shows that these tools, together, providesignificant improvements in computational efficiency for both online moving horizonbattery state estimation and online health-conscious model predictive battery con-trol. These benefits are demonstrated both in simulation and using an experimentalcase study.Two key facts motivate this dissertation. First, lithium-ion batteries are widelyused for different applications due to their low self-discharge rates, lack of memoryeffects, and high power/energy densities compared to traditional lead-acid and nickel-metal hydride batteries. Second, lithium-ion batteries are also vulnerable to agingand degradation mechanisms, such as lithium plating, some of which can lead tosafety issues. Conventional battery management systems (BMS) typically use model-free control strategies and therefore do not explicitly optimize the performance, lifespan, and cost of lithium-ion battery packs. They typically avoid internal damageby constraining externally-measured variables, such as battery voltage, current,and temperature. When pushed to charge a battery quickly without inducingexcessive damage, these systems often follow simple and potentially sub-optimalcharge/discharge trajectories, e.g., the constant-current/constant-voltage (CCCV)charging strategy. While the CCCV charging strategy is simple to implement,it suffers from its poor ability to explicitly control the internal variables causingbattery aging, such as side reaction overpotentials. Another disadvantage is theinability of this strategy to adapt to changes in battery dynamics caused by aging.Model-based control has the potential to alleviate many of the above limitationsof classical battery management systems. A model-based control system can estimate the internal state of a lithium-ion battery and use the estimated stateto adjust battery charging/discharging in a manner that avoids damaging sidereactions. By doing so, model-based control can (i) prolong battery life, (ii) improvebattery safety, (iii) increase battery energy storage capacity, (iv) decrease internaldamage/degradation, and (v) adapt to changes in battery dynamics resulting fromaging. These potential benefits are well-documented in the literature. However,one major challenge remains, namely, the computational complexity associatedwith online model-based battery state estimation and control. The goal of thisdissertation is to address this challenge by making five contributions to the literature.Specifically: Chapter 2 exploits the differential flatness of solid-phase lithium-ion batterydiffusion dynamics, together with pseudo-spectral optimization and diffusionmodel reformulation, to decrease the computational load associated withhealth-conscious battery trajectory optimization significantly. This contribu-tion forms a foundation for much of the subsequent work in this dissertation,but is limited to isothernal single-particle battery models with significanttime scale separation between anode- and cathode-side solid-phase diffusiondynamics. Chapter 3 extends the results of Chapter 2 in two ways. First , it exploitsthe law of conservation of charge to enable flatness-based, health-consciousbattery trajectory optimization for single particle battery models even in theabsence of time scale separation between the negative and positive electrodes.Second, it performs this optimization for a combined thermo-electrochemicalbattery model, thereby relaxing the above assumption of isothermal batterybehavior and highlighting the benefits of flatness-based optimization for anonlinear battery model. Chapter 4 presents a framework for flatness-based pseudo-spectral combinedstate and parameter estimation in lumped-parameter nonlinear systems.This framework enables computationally-efficient total least squares (TLS)estimation for lumped-parameter nonlinear systems. This is quite relevant topractical lithium-ion battery systems, where both battery input and outputmeasurements can be quite noisy. Chapter 5 utilizes the above flatness-based TLS estimation algorithm formoving horizon state estimation using a coupled thermo-electrochemicalequivalent circuit model of lithium-ion battery dynamics. Chapter 6 extends the battery estimation framework from Chapter 5 to enablemoving horizon, flatness-based TLS state estimation in thermo-electrochemical single-particle lithium-ion battery models, and demonstrates this frameworkusing laboratory experiments.The overall outcome of this dissertation is an integrated set of tools, all of themexploiting model reformulation, differential flatness, and pseudo-spectral methods,for computationally efficient online state estimation and health-conscious controlin lithium-ion batteries.
Real Time Optimal Control And State Estimation For Li Ion Batteries
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Author : Dhananjay Gupta (M.S. in Engineering)
language : en
Publisher:
Release Date : 2021
Real Time Optimal Control And State Estimation For Li Ion Batteries written by Dhananjay Gupta (M.S. in Engineering) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Li-Ion batteries are increasingly being looked at as a major alternative to fossil fuels in the transition towards clean energy. This has made created the necessity to be able to understand and predicted their behaviors – with the goal of elongating their life and ensuring safety of use. This thesis investigates the use of optimization-based state estimation and control methods on first-principle, physics-based models for the monitoring and real time control of batteries. Specifically, Moving Horizon Estimation in conjunction with Nonlinear Model Predictive Control applied to the Single Particle and Tank-in-Series Battery Models are investigated. First principle Li-Ion Battery Models consist of a set of coupled differential and algebraic equations. The constants in these equations are battery design parameters, which have been identified for an LGHG2 Cell by referring to relevant literature and conducting parameter estimation using gradient based methods. The two models’ equations are solved using numerical methods after spatial discretization. The optimization problems for state estimation and control are set up and tested offline. The same real time control framework is then deployed onto hardware application with a real battery. The real time control using this setup is tested on a Raspberry Pi, to gauge and optimally charge an LGHG2 3Ah Cell. The battery voltage and current is measured using a TI BQ40Z50 Battery Fuel Gauge, and the charging is done using a TI BQ25700A Buck Boost Charger. The optimization-based state estimation algorithm (MHE) can converge to the measured voltage and recover the model states based on real time current and voltage measurements received from the fuel gauge. The control algorithm (NMPC) can adjust the charging current as the battery nears the voltage setpoint, to prevent overcharging. The designed algorithm can also be easily modified for several objective functions, cell chemistries, and constraints. The novelty in this work is the application onto hardware, and closed loop, real time implementation of a Nonlinear Model Predictive Control algorithm without the use of any lookup tables, where optimization is conducted at each step to find an optimal control action
Battery State Estimation
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Author : Shunli Wang
language : en
Publisher:
Release Date : 2022
Battery State Estimation written by Shunli Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
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.