Topic Editors

Applied Energy Laboratory, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Frascati, Italy

Energy Storage and Conversion Systems, 2nd Volume

Abstract submission deadline
20 October 2024
Manuscript submission deadline
20 February 2025
Viewed by
2935

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Energy Storage and Conversion Systems”.

Energy storage and conversion are crucial topics for research and industry, especially from the perspective of a sustainable development. Scientific and technological progresses in these fields may improve the potential capabilities and efficiency in the use of energy both traditional, renewable and unconventional sources.

Energy storage technologies, such as batteries, fuel cells, supercapacitors (ultracapacitors), superconducting magnetic energy storage (SMES), combined with reductions in costs, are creating new scenarios and opportunities in the development and the market of energy generation, grids, industrial plants, complex systems and consumer electronics.

We would like to invite submissions to this Topic to collect the latest developments and applications in these interdisciplinary fields and provide a common framework to authors from different research areas.

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

  • Energy storage theory and applications;
  • Energy conversion theory and applications;
  • Power electronics and converters for smart grids, microgrids and electrical/hybrid vehicles;
  • Power converters for renewable sources, such as solar, wind, hydro and marine power;
  • High-voltage direct current (HVDC) grids and conversion systems;
  • Experimental techniques for characterization and diagnosis of energy storage and conversion systems;
  • Approaches and tools for modeling and simulation;
  • Batteries technologies, processes, materials, test and modeling;
  • Fuel cells and hydrogen-based systems;
  • Supercapacitors (ultracapacitors) and lithium-ion capacitors;
  • Superconducting magnetic energy storage (SMES);
  • Thermal energy storage, cogeneration and thermal management;
  • Combination and integration of several energy sources and storage solutions;
  • Control algorithms, including artificial intelligence tools;
  • Management systems, such as battery management systems (BMS);
  • Power quality, load management, peak shaving and back-up issues;
  • Energy harvesting and recovery;
  • Reliability, resilience and safety of complex systems and grids;
  • Technical-economical evaluations and market analyses.

Prof. Dr. Alon Kuperman
Dr. Alessandro Lampasi
Topic Editors

Keywords

  • energy storage
  • energy conversion
  • renewable energy
  • power generation
  • energy management
  • power systems
  • power electronics
  • power converters
  • smart grids
  • electrical vehicles
  • batteries
  • supercapacitors
  • fuel cells
  • electrical machines and drives
  • testing and modeling

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Processes
processes
3.5 4.7 2013 13.7 Days CHF 2400 Submit
Solar
solar
- - 2021 16.9 Days CHF 1000 Submit

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Published Papers (2 papers)

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21 pages, 6699 KiB  
Article
High-Speed Tracking Controller for Stable Power Control in Discontinuous Charging Systems
Electronics 2024, 13(1), 183; https://doi.org/10.3390/electronics13010183 - 31 Dec 2023
Viewed by 410
Abstract
The global population is rapidly increasing, and the urban population is on an even faster trend; therefore, the population density is expected to rise. As the number of people in cities grows, the demand for high-rise buildings is anticipated to increase to address [...] Read more.
The global population is rapidly increasing, and the urban population is on an even faster trend; therefore, the population density is expected to rise. As the number of people in cities grows, the demand for high-rise buildings is anticipated to increase to address the problem of limited land resources. Therefore, efficient energy management using distributed resources has become increasingly important. Elevators are a vital vertical means of transportation in high-rise buildings, and reducing the weight of their components can lead to favorable conditions for energy utilization and increased speed. Therefore, this study presents an elevator system that supplies power inside an elevator car by eliminating the traveling cable and applying a small-capacity energy storage system (ESS). Additionally, we propose a charging algorithm suitable for the proposed system. Generally, batteries have sensitive electrical properties among the distributed energy resources (DERs). Therefore, controlling the stable maintenance of the transient state of the charging current—even when the DC power is unstable or the load changes rapidly in a system requiring fast charging—is crucial. Owing to the nature of the elevator system to be applied, discontinuous charging is frequent, and the active and efficient management of the battery state of charge (SOC) may be challenging. In addition, since it is necessary to be able to charge as much as possible during a short discontinuous charging time, a current control algorithm with a stable and high-speed response is required. The proposed transient high-speed tracking controller (THSTC) is a method for tracking the time of applying an inductor’s excitation voltage without pulse–width modulation (PWM) switching, which is less sensitive to the controller gain values and has fast responsiveness as well as stable transient response characteristics. The proposed method has good dynamic characteristics with a simple control structure without a complex design, which is useful for systems with repeated discontinuous charging. We validate the performance and effectiveness of the proposed controller through simulations and experiments. Full article
(This article belongs to the Topic Energy Storage and Conversion Systems, 2nd Volume)
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23 pages, 5111 KiB  
Article
Spatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation
Electronics 2023, 12(12), 2598; https://doi.org/10.3390/electronics12122598 - 08 Jun 2023
Cited by 7 | Viewed by 1753
Abstract
Over the past ten years, breakthroughs in battery technology have dramatically propelled the evolution of electric vehicle (EV) technologies. For EV applications, accurately estimating the state-of-charge (SOC) is critical for ensuring safe operation and prolonging the lifespan of batteries, particularly under complex loading [...] Read more.
Over the past ten years, breakthroughs in battery technology have dramatically propelled the evolution of electric vehicle (EV) technologies. For EV applications, accurately estimating the state-of-charge (SOC) is critical for ensuring safe operation and prolonging the lifespan of batteries, particularly under complex loading scenarios. Despite progress in this area, modeling and forecasting the evaluation of multiphysics and multiscale electrochemical systems under realistic conditions using first-principles and atomistic calculations remains challenging. This study proposes a solution by designing a specialized Transformer-based network architecture, called Bidirectional Encoder Representations from Transformers for Batteries (BERTtery), which only uses time-resolved battery data (i.e., current, voltage, and temperature) as an input to estimate SOC. To enhance the Transformer model’s generalization, it was trained and tested under a wide range of working conditions, including diverse aging conditions (ranging from 100% to 80% of the nominal capacity) and varying temperature windows (from 35 °C to −5 °C). To ensure the model’s effectiveness, a rigorous test of its performance was conducted at the pack level, which allows for the translation of cell-level predictions into real-life problems with hundreds of cells in-series conditions possible. The best models achieve a root mean square error (RMSE) of less than 0.5 test error and approximately 0.1% average percentage error (APE), with maximum absolute errors (MAE) of 2% on the test dataset, accurately estimating SOC under dynamic operating and aging conditions with widely varying operational profiles. These results demonstrate the power of the self-attention Transformer-based model to predict the behavior of complex multiphysics and multiscale battery systems. Full article
(This article belongs to the Topic Energy Storage and Conversion Systems, 2nd Volume)
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