Emerging Trends in Advanced Materials and Technologies for Sustainable Energy Storage

Topic Information

Dear Colleagues,

This multidisciplinary topic focuses on the latest advances in energy storage technologies, with a specific emphasis on high energy density and high power density, safety, recycling, and the utilization of advanced in situ characterization tools and data-driven approaches. As the demand for efficient and sustainable energy storage solutions continues to grow, it is crucial to explore advancements in energy storage technologies and develop strategies to address safety concerns and enable effective recycling processes.

The multidisciplinary topic encompasses a wide range of materials, chemistries, and interfaces for lithium-ion batteries (LiBs), lithium metal batteries (LMBs), hybrid supercapacitors, and alternative battery systems such as sodium (Na), potassium (K), aluminum (Al), magnesium (Mg), and other ion- or element-based batteries. These technologies offer the potential for higher energy and power densities, enabling the development of more efficient and powerful energy storage systems.

Additionally, the multidisciplinary topic highlights the importance of recycling in the context of energy storage technologies. With the growing number of batteries reaching the end of their life cycles, the development of effective and sustainable recycling processes is critical to minimize environmental impact and recover valuable materials. Contributions discussing recycling strategies, methods for materials recovery, and life cycle assessments are encouraged.

Furthermore, the multidisciplinary topic promotes the use of advanced in situ characterization tools and data-driven approaches to enhance the understanding and advancement of next-generation energy storage systems. By leveraging real-time and high-resolution characterization techniques, researchers can gain valuable insights into battery materials, interfaces, and electrochemical processes. Data-driven approaches, including machine learning and computational modeling, can aid in the design and optimization of energy storage materials and devices.

Prof. Dr. Hongtao Sun
Dr. Jian Zhu
Prof. Dr. Junfei Liang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Batteries batteries	4.6	4.0	2015	19.7 Days	CHF 2700
Clean Technologies cleantechnol	4.1	6.1	2019	33.5 Days	CHF 1600
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600
Materials materials	3.1	5.8	2008	13.9 Days	CHF 2600
Nanomaterials nanomaterials	4.4	8.5	2010	14.1 Days	CHF 2400

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Published Papers (2 papers)

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All Batteries Clean Technol. Energies Materials Nanomaterials

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Open AccessReview

State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning

by Mei Li, Wenting Xu, Shiwen Zhang, Lina Liu, Arif Hussain, Enlai Hu, Jing Zhang, Zhiyu Mao and Zhongwei Chen

Materials 2025, 18(1), 145; https://doi.org/10.3390/ma18010145 - 2 Jan 2025

Cited by 1 | Viewed by 1449

Abstract

Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current [...] Read more.

Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current research focuses on health indicators associated with decreased capacity and increased internal resistance. However, due to the complex degradation mechanisms of lithium-ion batteries, the relationship between these mechanisms and health indicators has not been fully explored. This paper reviews a large number of literature sources. We discuss the application scenarios of different health factors, providing a reference for selecting appropriate health factors for state estimation. Additionally, the paper offers a brief overview of the models and machine learning algorithms used for health state estimation. We also delve into the application of health indicators in the health status assessment of battery management systems and emphasize the importance of integrating health factors with big data platforms for battery status analysis. Furthermore, the paper outlines the prospects for future development in this field. Full article

(This article belongs to the Topic Emerging Trends in Advanced Materials and Technologies for Sustainable Energy Storage)

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19 pages, 10414 KiB  

Open AccessArticle

Temperature Reduction as Operando Performance Recovery Procedure for Polymer Electrolyte Membrane Fuel Cells

by Qian Zhang, Mathias Schulze, Pawel Gazdzicki and Kaspar Andreas Friedrich

Energies 2024, 17(4), 774; https://doi.org/10.3390/en17040774 - 6 Feb 2024

Cited by 3 | Viewed by 1466

Abstract

To efficiently mitigate the reversible performance degradation of polymer electrolyte membrane fuel cells, it is crucial to thoroughly understand recovery effects. In this work, the effect of operando performance recovery by temperature reduction is evaluated. The results reveal that operando reduction in cell [...] Read more.

To efficiently mitigate the reversible performance degradation of polymer electrolyte membrane fuel cells, it is crucial to thoroughly understand recovery effects. In this work, the effect of operando performance recovery by temperature reduction is evaluated. The results reveal that operando reduction in cell temperature from 80 °C to 45 °C yields a performance recovery of 60–70% in the current density range below 1 A cm⁻² in a shorter time (1.5 h versus 10.5 h), as opposed to a known and more complex non-operando recovery procedure. Notably, the absolute recovered voltage is directly proportional to the total amount of liquid water produced during the temperature reduction. Thus, the recovery effect is likely attributed to a reorganization/rearrangement of the ionomer due to water condensation. Reduction in the charge transfer and mass transfer resistance is observed after the temperature reduction by electrochemical impedance spectroscopy (EIS) measurement. During non-operando temperature reduction (i.e., open circuit voltage (OCV) hold during recovery instead of load cycling) an even higher recovery efficiency of >80% was achieved. Full article

(This article belongs to the Topic Emerging Trends in Advanced Materials and Technologies for Sustainable Energy Storage)

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