Energy Storage and Conversion Systems, 2nd Volume

Abstract submission deadline

20 October 2024

Manuscript submission deadline

20 February 2025

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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

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 (3 papers)

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All Applied Sciences Energies Electronics Processes Solar

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11 pages, 3420 KiB  

Open AccessArticle

Measurement Error in Thermoelectric Generator Induced by Temperature Fluctuation

by Yanan Li, Hao Yang, Chuanbin Yu, Wenjie Zhou, Qiang Zhang, Haoyang Hu, Peng Sun, Jiehua Wu, Xiaojian Tan, Kun Song, Guoqiang Liu and Jun Jiang

Energies 2024, 17(5), 1036; https://doi.org/10.3390/en17051036 - 22 Feb 2024

Viewed by 466

Abstract

The thermal-electric conversion efficiency is a crucial metric for evaluating the performance of a thermoelectric generator (TEG). However, accurate measurement of this efficiency remains a significant challenge due to various factors that impact heat flow measurements. We have observed that temperature fluctuations during [...] Read more.

The thermal-electric conversion efficiency is a crucial metric for evaluating the performance of a thermoelectric generator (TEG). However, accurate measurement of this efficiency remains a significant challenge due to various factors that impact heat flow measurements. We have observed that temperature fluctuations during temperature control are the primary factor contributing to measurement errors in heat flow under vacuum conditions. To address this issue, we have developed a time-dependent theoretical model for the thermal-electric coupling of a TEG measurement system based on Fourier’s theory of heat conduction. This model allows us to investigate the effects of both temperature fluctuation and structural parameters on the measurement error of TEG performance. Furthermore, we have proposed an error correction scheme for TEG performance based on our theoretical and experimental findings. These insights provide a theoretical framework and technical guidance for more precise measurements of TEG performance. Full article

(This article belongs to the Topic Energy Storage and Conversion Systems, 2nd Volume)

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21 pages, 6699 KiB  

Open AccessArticle

High-Speed Tracking Controller for Stable Power Control in Discontinuous Charging Systems

by Sang-Kil Lim, Jin-Hyun Park, Hyang-Sig Jun, Kwang-Bok Hwang, Chan Hwangbo and Jung-Hwan Lee

Electronics 2024, 13(1), 183; https://doi.org/10.3390/electronics13010183 - 31 Dec 2023

Viewed by 552

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  

Open AccessArticle

Spatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation

by Dapai Shi, Jingyuan Zhao, Zhenghong Wang, Heng Zhao, Junbin Wang, Yubo Lian and Andrew F. Burke

Electronics 2023, 12(12), 2598; https://doi.org/10.3390/electronics12122598 - 08 Jun 2023

Cited by 9 | Viewed by 2083

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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