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Topology Optimization of Special Power Supply and Balance Control of Distribution System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 816

Special Issue Editors


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Guest Editor
State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: new energy generation grid-connected access technology; micro-grid and distributed energy technology; modeling analysis and control of power electronic circuits and systems; bifurcation and chaos in electrical and electronic engineering
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Guest Editor
Space Research Institute of Electronics and Information Technology, China Academy of Space Technology, Xi’an 710000, China
Interests: topology; modeling; simulation and control of DC power supply in aerospace supply

Special Issue Information

Dear Colleagues,

With the development of science and technology, the demand for power supply is growing rapidly, and distributed systems becomes a common form of power supply, so that these points have been attracted more and more concentration from researchers. In particular, applications like medical equipment, power test, aerospace, etc., need lots of special power supply and distributed systems to satisfy their requirements. As is well known, the topology is the most critical factor for a special power supply, and the balance control is vital for distributed systems; therefore, topology optimization of special power supplies and balance control of distribution systems is a hot topic. In particular, the emergence of new circuit elements, including memristors, meminductors, memcapacitors, fractional-order circuit elements, etc., brings new opportunities and challenges to special power supply and distribution systems to meet the requirements for actual industrial applications.

This Special Issue aims to gather research on the topic of topology optimization of special power supplies and balance control of distribution systems.

Topics of interest for publication include, but are not limited to:

  • Novel topologies and topology optimization of special power supplies and distribution systems;
  • Modeling and analysis of special power supplies and distribution systems;
  • Bifurcation and chaos in the special power supplies and distribution systems;
  • Control methods for the special power supplies and distribution systems;
  • Fractional-order the special power supplies and distribution systems;
  • Fractional-order control and its application in the special power supplies and distribution systems;
  • Special power supplies and distribution systems with memristors, meminductors, or memcapacitors.

Dr. Faqiang Wang
Dr. Jing Li
Guest Editors

Manuscript Submission Information

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Keywords

  • special power supply
  • distribution system
  • novel topology design and optimization
  • modelling and analysis
  • novel control methods
  • fractional-order elements
  • memristor, meminductor, and memcapacitor
  • fractional order control
  • bifurcation and chaos

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Published Papers (1 paper)

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Research

21 pages, 4142 KiB  
Article
A Comparative Study of Data-Driven Early-Stage End-of-Life Classification Approaches for Lithium-Ion Batteries
by Xuelu Wang, Jianwen Meng and Toufik Azib
Energies 2024, 17(17), 4485; https://doi.org/10.3390/en17174485 - 6 Sep 2024
Viewed by 612
Abstract
Lithium-ion batteries are the most widely used as energy storage devices in electric mobility applications. However, due to complex electrochemical processes of battery degradation, it is challenging to predict accurately the battery end-of-life (EOL) to ensure their reliability, safety, and extended usage. In [...] Read more.
Lithium-ion batteries are the most widely used as energy storage devices in electric mobility applications. However, due to complex electrochemical processes of battery degradation, it is challenging to predict accurately the battery end-of-life (EOL) to ensure their reliability, safety, and extended usage. In this context, the introduction of machine learning techniques can provide relevant solutions based on data collection and analysis. Indeed, we compared in this study the prediction performance of numerous machine learning approaches that predict if the battery EOL bypasses a predefined threshold. Based on the variation of different indicators during the first several hundred cycles, such as charge and discharge capacity, internal resistance, and energy efficiency, extensive numerical tests have been executed and compared in terms of accuracy score, precision score, recall score, etc. All the studied machine learning approaches are trained and validated using an open-access database of 124 commercial lithium iron phosphate/graphite cells cycled under different fast-charging conditions. As a result, the classification prediction performance score reached up to 98.74% depending on the percentage of data and cycles used for training and validation as well as the predefined EOL threshold. The comparative results can be used to improve the existing health-aware energy management strategy by taking the state-of-health (SOH) of batteries into consideration. Overall, the presented research findings are relevant to battery system reliability and safety engineering. Full article
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