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.

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

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

Thorough reference on technologies, designs, and strategies for electric and hybrid electric vehicles, featuring contributions from international experts Designed for readers who need to review different types of electric and hybrid vehicle designs and strategies in a single book, Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling provides a broad overview of the field with additional resources to explore individual topics in greater depth. Abstracts, case studies, references to key data, and relevant numerical simulations are included throughout the text to aid in reader comprehension. This book introduces the global landscape of hybrid and electric vehicles, covering the available technologies from both a mechanical and electrical engineering perspective, presenting mathematical aspects of modeling and analysis, and surveying emerging trends and economic impacts. It also explains all fundamentals, regulations, policies, perceptions, and market competition aspects of intelligent electric vehicles, as well as how smart electric and hybrid vehicles can be utilized to reduce harmful emissions and reliance on fossil fuels over the lifecycle of a vehicle. Edited by a team of highly qualified academics, with contributions by an array of international experts, Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling includes information on: Electric machine and inverter designs, maximum speed considerations, component cooling, power density, and material performance Battery systems, fuel cells, plug-in vehicles, mechanical drives and storage systems, and the role of power electronics tools The impact of trends and technologies like AI, machine vision, and digital twins, as well as related cyber security considerations Optimization of manufacturing waste, charging stations, sensing control, road trajectory prediction, and navigation systems Electrical interfaces to protect against electric shock and cost effectiveness compared to gasoline-powered vehicles Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling is an essential reference on the subject for mechanical engineers, industrial engineers, and academic researchers working in the automotive sector. It is also an ideal learning resource for post-graduate students in the automotive field.

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.

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.