Download or read book Long Term Health State Estimation of Energy Storage Lithium Ion Battery Packs written by Qi Huang and published by Springer Nature. This book was released on 2023-08-18 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Modeling and State Estimation of Lithium Ion Battery Packs for Application in Battery Management Systems written by Manoj Mathew and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As lithium-ion (Li-Ion) battery packs grow in popularity, so do the concerns of its safety, reliability, and cost. An efficient and robust battery management system (BMS) can help ease these concerns. By measuring the voltage, temperature, and current for each cell, the BMS can balance the battery pack, and ensure it is operating within the safety limits. In addition, these measurements can be used to estimate the remaining charge in the battery (state-of-charge (SOC)) and determine the health of the battery (state-of-health (SOH)). Accurate estimation of these battery and system variables can help improve the safety and reliability of the energy storage system (ESS). This research aims to develop high-fidelity battery models and robust SOC and SOH algorithms that have low computational cost and require minimal training data. More specifically, this work will focus on SOC and SOH estimation at the pack-level, as well as modeling and simulation of a battery pack. An accurate and computationally efficient Li-Ion battery model can be highly beneficial when developing SOC and SOH algorithms on the BMS. These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effect of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out a large number of experiments, which can be too costly for many BMS manufacturers. Therefore, the first contribution of this research is to develop a comprehensive battery model, where the ECM parameter surface is generated using a set of carefully designed experiments. This technique is compared with existing approaches from literature, and it is shown that by using the proposed method, the same degree of accuracy can be obtained while requiring significantly less experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out a large number of experiments. Once a comprehensive model has been developed for SIL and HIL testing, research was carried out in advancing SOH and SOC algorithms. With respect to SOH, research was conducted in developing a steady and reliable SOH metric that can be determined at the cell level and is stable at different battery operating conditions. To meet these requirements, a moving window direct resistance estimation (DRE) algorithm is utilized, where the resistance is estimated only when the battery experiences rapid current transients. The DRE approach is then compared with more advanced resistance estimation techniques such as extended Kalman filter (EKF) and recursive least squares (RLS). It is shown that by using the proposed algorithm, the same degree of accuracy can be achieved as the more advanced methods. The DRE algorithm does, however, have a much lower computational complexity and therefore, can be implemented on a battery pack composed of hundreds of cells. Research has also been conducted in converting these raw resistance values into a stable SOH metric. First, an outlier removal technique is proposed for removing any outliers in the resistance estimates; specifically, outliers that are an artifact of the sampling rate. The technique involves using an adaptive control chart, where the bounds on the control chart change as the internal resistance of the battery varies during operation. An exponentially weighted moving average (EWMA) is then applied to filter out the noise present in the raw estimates. Finally, the resistance values are filtered once more based on temperature and battery SOC. This additional filtering ensures that the SOH value is independent of the battery operating conditions. The proposed SOH framework was validated over a 27-day period for a lithium iron phosphate (LFP) battery. The results show an accurate estimation of battery resistance over time with a mean error of 1.1% as well as a stable SOH metric. The findings are significant for BMS developers who have limited computational resources but still require a robust and reliable SOH algorithm. Concerning SOC, most publications in literature examine SOC estimation at the cell level. Determining the SOC for a battery pack can be challenging, especially an estimate that behaves logically to the battery user. This work proposes a three-level approach, where the final output from the algorithm is a well-behaved pack SOC estimate. The first level utilizes an EKF for estimating SOC while an RLS approach is used to adapt the model parameters. To reduce computational time, both algorithms will be executed on two specific cells: the first cell to charge to full and the first cell to discharge to empty. The second level consists of using the SOC estimates from these two cells and estimating a pack SOC value. Finally, a novel adaptive coulomb counting approach is proposed to ensure the pack SOC estimate behaves logically. The accuracy of the algorithm is tested using a 40 Ah Li-Ion battery. The results show that the algorithm produces accurate and stable SOC estimates. Finally, this work extends the developed comprehensive battery model to examine the effect of replacing damaged cells in a battery pack with new ones. The cells within the battery pack vary stochastically, and the performance of the entire pack is evaluated under different conditions. The results show that by changing out cells in the battery pack, the SOH of the pack can be maintained indefinitely above a specific threshold value. In situations where the cells are checked for replacement at discrete intervals, referred to as maintenance event intervals, it is found that the length of the interval is dependent on the mean time to failure of the individual cells. The simulation framework, as well as the results from this paper, can be utilized to better optimize Li-ion battery pack design in electric vehicles (EVs) and make long-term deployment of EVs more economically feasible.

Download or read book State Estimation Strategies in Lithium ion Battery Management Systems written by Shunli Wang and published by Elsevier. This book was released on 2023-07-14 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Multidimensional Lithium Ion Battery Status Monitoring written by Shunli Wang and published by CRC Press. This book was released on 2022-12-28 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Battery System Modeling written by Shunli Wang and published by Elsevier. This book was released on 2021-06-23 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Explains how to model battery systems, including equivalent, electrical circuit and electrochemical nernst modeling Includes comprehensive coverage of battery state estimation methods, including state of charge estimation, energy prediction, power evaluation and health estimation Provides a dedicated chapter on active control strategies

Download or read book Modeling and State Estimation of Automotive Lithium Ion Batteries written by Shunli Wang and published by CRC Press. This book was released on 2024-07-16 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to evaluate and improve the state of charge (SOC) and state of health (SOH) of automotive lithium-ion batteries. The authors first introduce the basic working principle and dynamic test characteristics of lithium-ion batteries. They present the dynamic transfer model, compare it with the traditional second-order reserve capacity (RC) model, and demonstrate the advantages of the proposed new model. In addition, they propose the chaotic firefly optimization algorithm and demonstrate its effectiveness in improving the accuracy of SOC and SOH estimation through theoretical and experimental analysis. The book will benefit researchers and engineers in the new energy industry and provide students of science and engineering with some innovative aspects of battery modeling.

Download or read book Neural Network Based State of Charge and State of Health Estimation written by Qi Huang and published by Cambridge Scholars Publishing. This book was released on 2023-11-16 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: To deal with environmental deterioration and energy crises, developing clean and sustainable energy resources has become the strategic goal of the majority of countries in the global community. Lithium-ion batteries are the modes of power and energy storage in the new energy industry, and are also the main power source of new energy vehicles. State-of-charge (SOC) and state-of-health (SOH) are important indicators to measure whether a battery management system (BMS) is safe and effective. Therefore, this book focuses on the co-estimation strategies of SOC and SOH for power lithium-ion batteries. The book describes the key technologies of lithium-ion batteries in SOC and SOH monitoring and proposes a collaborative optimization estimation strategy based on neural networks (NN), which provide technical references for the design and application of a lithium-ion battery power management system. The theoretical methods in this book will be of interest to scholars and engineers engaged in the field of battery management system research.

Download or read book Computationally Efficient Online Model Based Control and Estimation for Lithium ion Batteries written by Ji Liu and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Intelligent Algorithms in Software Engineering written by Radek Silhavy and published by Springer Nature. This book was released on 2020-08-08 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented.

Download or read book Handbook on Battery Energy Storage System written by Asian Development Bank and published by Asian Development Bank. This book was released on 2018-12-01 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook serves as a guide to deploying battery energy storage technologies, specifically for distributed energy resources and flexibility resources. Battery energy storage technology is the most promising, rapidly developed technology as it provides higher efficiency and ease of control. With energy transition through decarbonization and decentralization, energy storage plays a significant role to enhance grid efficiency by alleviating volatility from demand and supply. Energy storage also contributes to the grid integration of renewable energy and promotion of microgrid.

Download or read book Innovative Methods and Techniques in New Electric Power Systems written by David Gao and published by Frontiers Media SA. This book was released on 2023-04-03 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fuzzy Filter Based State of Energy Estimation for Lithium Ion Batteries written by Shunli Wang and published by Cambridge Scholars Publishing. This book was released on 2024-03-21 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Awareness of the safety issues of lithium-ion batteries is crucial in the development of new energy technologies, and real-time and high-precision State of Energy (SOE) estimation is not only a prerequisite for battery safety, but also serves as the basis for predicting the remaining driving range of electric vehicles and aircrafts. In order to achieve real-time and accurate estimation of the energy state of lithium-ion batteries, this book improves the calculation method of the open-circuit voltage in the traditional second-order RC equivalent circuit model. It also combines a fuzzy controller and a dual-weighted multi-innovation algorithm to optimize the traditional Centralized Kalman Filter (CKF) algorithm in terms of the aspects of convergence speed, estimation accuracy, and algorithm robustness. This enables the precise estimation of SOE and the maximum available energy. The content of this book provides theoretical support for the development of new energy initiatives.

Download or read book Machine Intelligence for Research and Innovations written by Om Prakash Verma and published by Springer Nature. This book was released on 2024-02-13 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a collection of high-quality peer-reviewed research papers presented in the First International Conference on Machine Intelligence for Research and Innovations (MAiTRI 2023 Summit), held at Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India during 1 – 3 September 2023. This book focuses on recent advancement in the theory and realization of machine intelligence (MI) and their tools and growing applications such as machine learning, deep learning, quantum machine learning, real-time computer vision, pattern recognition, natural language processing, statistical modelling, autonomous vehicles, human interfaces, computational intelligence, and robotics.

Download or read book Mathematical Modeling of Lithium Batteries written by Krishnan S. Hariharan and published by Springer. This book was released on 2017-12-28 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is unique to be the only one completely dedicated for battery modeling for all components of battery management system (BMS) applications. The contents of this book compliment the multitude of research publications in this domain by providing coherent fundamentals. An explosive market of Li ion batteries has led to aggressive demand for mathematical models for battery management systems (BMS). Researchers from multi-various backgrounds contribute from their respective background, leading to a lateral growth. Risk of this runaway situation is that researchers tend to use an existing method or algorithm without in depth knowledge of the cohesive fundamentals—often misinterpreting the outcome. It is worthy to note that the guiding principles are similar and the lack of clarity impedes a significant advancement. A repeat or even a synopsis of all the applications of battery modeling albeit redundant, would hence be a mammoth task, and cannot be done in a single offering. The authors believe that a pivotal contribution can be made by explaining the fundamentals in a coherent manner. Such an offering would enable researchers from multiple domains appreciate the bedrock principles and forward the frontier. Battery is an electrochemical system, and any level of understanding cannot ellipse this premise. The common thread that needs to run across—from detailed electrochemical models to algorithms used for real time estimation on a microchip—is that it be physics based. Build on this theme, this book has three parts. Each part starts with developing a framework—often invoking basic principles of thermodynamics or transport phenomena—and ends with certain verified real time applications. The first part deals with electrochemical modeling and the second with model order reduction. Objective of a BMS is estimation of state and health, and the third part is dedicated for that. Rules for state observers are derived from a generic Bayesian framework, and health estimation is pursued using machine learning (ML) tools. A distinct component of this book is thorough derivations of the learning rules for the novel ML algorithms. Given the large-scale application of ML in various domains, this segment can be relevant to researchers outside BMS domain as well. The authors hope this offering would satisfy a practicing engineer with a basic perspective, and a budding researcher with essential tools on a comprehensive understanding of BMS models.

Download or read book Advances in Lithium Ion Batteries for Electric Vehicles written by Haifeng Dai and published by Elsevier. This book was released on 2024-02-26 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and Lifetime Prediction examines the electrochemical nature of lithium-ion batteries, including battery degradation mechanisms and how to manage the battery state of health (SOH) to meet the demand for sustainable development of electric vehicles. With extensive case studies, methods and applications, the book provides practical, step-by-step guidance on battery tests, degradation mechanisms, and modeling and management strategies. The book begins with an overview of Li-ion battery aging and battery aging tests before discussing battery degradation mechanisms and methods for analysis. Further methods are then presented for battery state of health estimation and battery lifetime prediction, providing a range of case studies and techniques. The book concludes with a thorough examination of lifetime management strategies for electric vehicles, making it an essential resource for students, researchers, and engineers needing a range of approaches to tackle battery degradation in electric vehicles. Evaluates the cause of battery degradation from the material level to the cell level Explains key battery basic lifetime test methods and strategies Presents advanced technologies of battery state of health estimation

Download or read book Smart Battery Management for Enhanced Safety written by Zhongbao Wei and published by Springer Nature. This book was released on with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Power Engineering and Intelligent Systems written by Vivek Shrivastava and published by Springer Nature. This book was released on 2023-12-15 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents a collection of the high-quality research articles in the field of power engineering, grid integration, energy management, soft computing, artificial intelligence, signal and image processing, data science techniques, and their real-world applications. The papers are presented at International Conference on Power Engineering and Intelligent Systems (PEIS 2023), held during June 24–25, 2023, at National Institute of Technology Delhi, India.