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| Author | : Shi Li |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : Algorithms |
| ISBN | : |
| Author | : Jiahao Li |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2016 |
| Genre | : |
| ISBN | : |
| Author | : Shunli Wang |
| Publisher | : CRC Press |
| Total Pages | : 355 |
| Release | : 2022-12-28 |
| Genre | : Technology & Engineering |
| ISBN | : 1000799565 |
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.
| Author | : Wladislaw Waag |
| Publisher | : |
| Total Pages | : 242 |
| Release | : 2014 |
| Genre | : |
| ISBN | : 9783844029765 |
| Author | : Shunli Wang |
| Publisher | : Cambridge Scholars Publishing |
| Total Pages | : 141 |
| Release | : 2024-03-21 |
| Genre | : Technology & Engineering |
| ISBN | : 1527570673 |
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.
| Author | : Shunli Wang |
| Publisher | : Elsevier |
| Total Pages | : 377 |
| Release | : 2023-07-14 |
| Genre | : Business & Economics |
| ISBN | : 0443161615 |
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
| Author | : Walter van Schalkwijk |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 514 |
| Release | : 2007-05-08 |
| Genre | : Science |
| ISBN | : 0306475081 |
In the decade since the introduction of the first commercial lithium-ion battery research and development on virtually every aspect of the chemistry and engineering of these systems has proceeded at unprecedented levels. This book is a snapshot of the state-of-the-art and where the work is going in the near future. The book is intended not only for researchers, but also for engineers and users of lithium-ion batteries which are found in virtually every type of portable electronic product.
| Author | : Shunli Wang |
| Publisher | : Elsevier |
| Total Pages | : 356 |
| Release | : 2021-06-23 |
| Genre | : Science |
| ISBN | : 0323904335 |
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
| Author | : Quan Ouyang |
| Publisher | : Springer Nature |
| Total Pages | : 182 |
| Release | : 2023-01-01 |
| Genre | : Technology & Engineering |
| ISBN | : 9811970599 |
In this book, the most state-of-the-art advanced model-based charging control technologies for lithium-ion batteries are explained from the fundamental theories to practical designs and applications, especially on the battery modelling, user-involved, and fast charging control algorithm design. Moreover, some other necessary design considerations, such as battery pack charging control with centralized and distributed structures, are also introduced to provide excellent solutions for improving the charging performance and extending the lifetime of the batteries/battery packs. Finally, some future directions are mentioned in brief. This book summarizes the model-based charging control technologies from the cell level to the battery pack level. From this book, readers interested in battery management can have a broad view of modern battery charging technologies. Readers who have no experience in battery management can learn the basic concept, analysis methods, and design principles of battery charging systems. Even for the readers who are occupied in this area, this book also provides rich knowledge on engineering applications and future trends of battery charging technologies.
| Author | : Shijie Tong |
| Publisher | : |
| Total Pages | : |
| Release | : 2014 |
| Genre | : |
| ISBN | : 9781321364101 |
Energy storage is one of society's grand challenges for the 21st century. Lithium ion batteries (LIBs) are widely used in mobile devices, transportation, and stationary energy storages due to lowering cost combined with excellent power/energy density as well as cycle durability. The need for a battery management system (BMS) arises from a demand to improve cycle life, assure safety, and optimize the full pack performance. In this work, we proposed a model based battery on-line state of charge (SoC) and state of health (SoH) estimator for LIBs. The estimator incorporates a comprehensive Equivalent Circuit Model (ECM) as reference, an Extended Kalman Filter (EKF) as state observer, a Recursive Least Square (RLS) algorithm as parameter identifier, and Parameter Varying Approach (PVA) based optimization algorithms for the parameter function regressions. The developed adaptive estimator was applied to a 10kW smart grid energy storage application using retired electric vehicle batteries. The estimator exhibits a high numerical efficiency as well as an excellent accuracy in estimating SoC and SoH. The estimator also provides a novel method to optimize the correlation between battery open circuit voltage (OCV) and SoC, which further improves states estimation accuracy.
