Smart Safety Management of Energy Storage Batteries addresses battery management in new power systems which is an important component of the new generation of information technology and power systems. This book covers the application of this new type of power storage as well as power system identification modeling, intelligent energy storage battery status evaluation, and key technologies in intelligent management monitoring. Written for researchers, engineers, and students studying related areas, this book supports research in control science and control, automation, and electrical engineering, and serves as a technical reference for the application of new electric energy storage battery science and technology.
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1. Introduction
2. Power Storage Battery Testing
3. Smart Energy Modeling Analysis
4. Intelligent Core Algorithm for Predicting the State of Energy Storage Batteries
5. SOC Estimation of Energy Storage Battery Based on LSTM Neural Network
6. Data-driven SOH Estimation for Energy Storage Battery Clusters
7. Battery Peak Power Eestimation Based on Long and Short Term Memory Networks
8. Design and Optimization of Energy State Assessment Algorithms for Energy Storage Batteries
9. Improved Firefly Optimized Method for Lithium-ion Batteries of the Co-Estimation of SOC and SOH
10. Joint Estimation of SOC and SOP for Lithium-ion Battery Based on H∞ Filtering
11. Battery RUL Prediction Based on Multicore Correlation Vector Machine
12. Intelligent Balancing Management of New Power Storage Batteries
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Covers new electric energy storage battery system engineering with a specific focus on the intelligent technology of energy storage battery management systems
Contains technical references for system design and application
Addresses battery equivalent modeling, including electrical circuit modeling and parameter identification theory
Includes coverage of battery state estimation methods such as state of charge estimation, state of health estimation, and state-of-charge and state-of-health co-estimation
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Produktdetaljer
Utgiver
Elsevier - Health Sciences Division; Elsevier - Health Sciences Division
Om bidragsyterne
Shunli Wang is a professor at the Southwest University of Science and Technology, China. He is an authoritative expert in the field of new energy research. He is the head of DTlab, modeling, and state estimation strategy research for lithium-ion batteries. He has undertaken more than 40 projects and 30 patents, published more than 100 research papers as well as won 20 awards such as the Young Scholar, and Science & Technology Progress Awards. Yanxin Xie is a PhD student of Southwest University of Science and Technology, China. Her research direction is new energy measurement and control, and she has long been engaged in modeling, and state estimation strategy research for lithium-ion batteries. She has published 4 papers, applied for 6 intellectual property rights, granted 4 authorizations, participated in 6 projects, won 1 national scholarship, 1 first-class scholarship, and 1 second-class scholarship.
Guangchen Liu, Professor at Inner Mongolia University of Technology. Mainly engaged in teaching and research work in the fields of new energy generation and energy storage, power electronics, and power transmission. Hosted multiple projects such as the National Natural Science Foundation, Inner Mongolia Science and Technology Major Special Project, and Inner Mongolia Science and Technology Plan, published over 70 academic papers, and authored 3 monographs/textbooks. The research achievements are mainly used to solve the problem of high-quality power supply in remote areas. Huang Qi is a professor and doctoral supervisor, IEEE Fellow, IETF fellow, expert enjoying special government allowance of the State Council, candidate of New Century Excellent Talents Support Program of Ministry of Education, academic and technical leader of Sichuan Province, head of Youth Science and Technology Innovation Team of Sichuan Province, candidate of "Tianfu Qingcheng Innovation Leading Talents Program" of Sichuan Province. His main research fields are: new power energy systems, science and technology innovation strategy, and development planning etc. He has undertaken more than 20 national and provincial projects such as National Key R & D Plan and National Natural Science Foundation, and won 1 China Patent Excellence Award, 1 First Prize of China Instrument Society, 1 First Prize/Second Prize of Sichuan Province Science and Technology Progress Award, and 1 Second Prize of Excellent Scientific and Technological Achievements of Ministry of Education. He has published more than 300 academic papers, including more than 200 SCI papers, published 5 monographs, including 2 Wiley-IEEE monographs, applied for more than 100 patents, and obtained more than 80 authorized national invention patents and 2 US patents. He served as a member of the Expert Group on Science and Technology Innovation Planning in the Energy Field of the 13th Five-Year Plan of the Ministry of Science and Technology, the leader of the Expert Group on the 14th Five-Year Plan in the Energy and Chemical Industry Field of Sichuan Province, the convener of the 13th Five-Year Plan in the New Energy Field of Sichuan Province, and the leader of the Expert Group on "Clean Energy" in the "5+1" Modern Industrial System of Sichuan Province. He served as the chairman of several high-level international conferences, such as the 2019 IEEPES Innovative Smart Grid Technology and the 2022 IEEEI 2.
Yujie Wang is an associate professor at the Department of Automation, University of Science and Technology of China. His research interests include new energy vehicle technology, battery safety management, digital twin and AI application in energy system. He received his Ph.D. degree in Control science and engineering from the University of Science and Technology of China in 2017. He has co-authored over 60 SCI journal papers on battery-related topics. His research interests include energy-saving and new energy vehicle technology, complex system modeling, simulation and control, fuel cell system management, and optimal control. Gexiang Zhang is Foreign Academician of the Russian Academy of Natural Sciences, IET Fellow, and President of the International Membrane Computing Society. He mainly engages in research work in artificial intelligence, intelligent robots, smart grids, and intelligent control. He pioneered the establishment of multiple research platforms such as IMCS and JMC; The publication of 'Application of Membrane Computing in Real Life' fills the gap in practical works in the field of membrane computing worldwide. Carlos Fernandez is a senior lecturer at Robert Gordon University, Scotland, UK. He received his Ph.D. in Electrocatalytic Reactions from The University of Hull and then worked as a Consultant Technologist in Hull and a post-doctoral position in Manchester. His research interests include Analytical Chemistry, Sensors and Materials, and Renewable Energy.