Energy Storage and Conversion Systems, 2nd Volume

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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	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Processes processes	2.8	5.1	2013	14.4 Days	CHF 2400	Submit
Solar solar	-	-	2021	27.4 Days	CHF 1000	Submit

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Published Papers (7 papers)

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All Applied Sciences Energies Electronics Processes Solar

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10 pages, 2750 KiB  

Open AccessArticle

Novel Q-Carbon Anodes for Sodium-Ion Batteries

by Saurabh Prakash Pethe, Siba Sundar Sahoo, Arvind Ganesan, Harry M. Meyer III, Xiao-Guang Sun, Jagdish Narayan and Mariappan Parans Paranthaman

Appl. Sci. 2024, 14(22), 10679; https://doi.org/10.3390/app142210679 - 19 Nov 2024

Viewed by 253

Abstract

The lack of a standard anode for sodium-ion batteries (SIBs) has greatly hindered their applications. Herein, we show that a novel phase of carbon, namely Q-carbon, is an effective anode material for sodium-ion batteries. The Q-carbon, which is a metastable phase of carbon [...] Read more.

The lack of a standard anode for sodium-ion batteries (SIBs) has greatly hindered their applications. Herein, we show that a novel phase of carbon, namely Q-carbon, is an effective anode material for sodium-ion batteries. The Q-carbon, which is a metastable phase of carbon consisting of about 80% sp³- and 20% sp²-bonded carbon, is synthesized by nonequilibrium pulsed laser annealing and arc-discharge methods. Two types of Q-carbons, Q1 and Q2, were evaluated as anode material for SIBs. Q1 had a slow quench and was used as the control, whereas Q2 was Q-carbon with a rapid quenching. Q1 exhibits a high initial columbic efficiency of 81% and a low-capacity retention of less than 60%, whereas Q2 has a low initial columbic efficiency of 58% and a high-capacity retention of 81%. Q2 exhibits a stable capacity of 168 mAh·g⁻¹ at a cycling rate of C/3 (124 mA·g⁻¹), which is comparable to other hard carbon anodes reported in the literature. This unique synthesis method opens a pathway for the further tuning of Q-carbon with higher trapping/charging of Na⁺ ions in improved SIBs. Full article

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

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14 pages, 6104 KiB  

Open AccessArticle

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

by Sihan Sun, Minming Gu and Tuoqi Liu

Electronics 2024, 13(13), 2501; https://doi.org/10.3390/electronics13132501 - 26 Jun 2024

Cited by 1 | Viewed by 1430

Abstract

Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation [...] Read more.

Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method. Full article

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

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17 pages, 7010 KiB  

Open AccessArticle

Numerical and Experimental Investigation on Performance of Thermal Energy Storage Integrated Micro-Cold Storage Unit

by Sreelekha Arun, Rushikesh J. Boche, Prahas Nambiar, Prince Ekka, Pratham Panalkar, Vaibhav Kumar, Anindita Roy and Stefano Landini

Appl. Sci. 2024, 14(12), 5166; https://doi.org/10.3390/app14125166 - 14 Jun 2024

Viewed by 1044

Abstract

Preservation of perishable food produce is a major concern in the cold chain supply system. Development of an energy-efficient on-farm cold storage facility, hence, becomes essential. Integration of thermal storage into a vapor compression refrigeration (VCR)-driven cold room is a promising technology that [...] Read more.

Preservation of perishable food produce is a major concern in the cold chain supply system. Development of an energy-efficient on-farm cold storage facility, hence, becomes essential. Integration of thermal storage into a vapor compression refrigeration (VCR)-driven cold room is a promising technology that can reduce power consumption and act as a thermal backup. However, designing a latent heat energy storage heat exchanger encounters challenges, such as low thermal conductivity of phase change materials (PCMs) and poor heat exchanger efficiencies, leading to ineffective charging–discharging cycles. The current study investigates the effect of the integration of a Phase Change Material (PCM) in terms of the selection of the PCM, the optimal positioning of the PCM heat exchanger, and the selection of heat exchanger encapsulation material. Numerical analysis was undertaken using 3D Experience software (version: 2024x.D31.R426rel.202403212040) by creating a 3D model of a 3.4 m³ micro-cold storage unit to understand the inner temperature distribution profile. Further, the experimental setup was developed, and tests were conducted, during which the energy consumption of 1.1 kWh was recorded for the total compressor run time of 1 h. Results indicated that an improved cooling effect was achieved by positioning the PCM trays on the wall opposite the evaporator. It is seen that a temperature difference in the range of 5 to 7 °C exists between the phase change temperature of PCM and the optimal storage temperature depending on the encapsulation material. Hence, PCM selection for thermal storage applications would have an important bearing on the material and configuration of the PCM encapsulation. Full article

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

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20 pages, 3331 KiB  

Open AccessArticle

Innovative Design of Solid-State Hydrogen Storage and Proton Exchange Membrane Fuel Cell Coupling System with Enhanced Cold Start Control Strategy

by Jianhua Gao, Su Zhou, Lei Fan, Gang Zhang, Yongyuan Jiang, Wei Shen and Shuang Zhai

Appl. Sci. 2024, 14(10), 4068; https://doi.org/10.3390/app14104068 - 10 May 2024

Viewed by 1250

Abstract

This paper presents an innovative thermally coupled system architecture with a parallel coolant-heated metal hydride tank (MHT) designed to satisfy the hydrogen supply requirements of proton exchange membrane fuel cell s(PEMFCs). This design solves a problem by revolutionising the cold start capability of [...] Read more.

This paper presents an innovative thermally coupled system architecture with a parallel coolant-heated metal hydride tank (MHT) designed to satisfy the hydrogen supply requirements of proton exchange membrane fuel cell s(PEMFCs). This design solves a problem by revolutionising the cold start capability of PEMFCs at low temperatures. During the design process, LaNi₅ was selected as the hydrogen storage material, with thermodynamic and kinetic properties matching the PEMFC operating conditions. Afterwards, the MHT and thermal management subsystem were customised to integrate with the 70 kW PEMFC system to ensure optimal performance. Given the limitations of conventional high-pressure gaseous hydrogen storage for cold starting, this paper provides insights into the challenges faced by the PEMFC-MH system and proposes an innovative cold start methodology that combines internal self-heating and externally assisted preheating techniques, aiming to optimise cold start time, energy consumption, and hydrogen utilisation. The results show that the PEMFC-MH system utilises the heat generated during hydrogen absorption by the MHT to preheat the PEMFC stack, and the cold start time is only 101 s, which is 59.3% shorter compared to that of the conventional method. Meanwhile, the cold start energy consumption is reduced by 62.4%, achieving a significant improvement in energy efficiency. In conclusion, this paper presents a PEMFC-MH system design that achieves significant progress in terms of time saving, energy consumption, and hydrogen utilisation. Full article

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

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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

Cited by 1 | Viewed by 1015

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

Cited by 1 | Viewed by 865

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 - 8 Jun 2023

Cited by 19 | Viewed by 3077

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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