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Advanced Electrode Materials for Lithium-Ion and Sodium-Ion Secondary Batteries

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Energy Materials".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 415

Special Issue Editors

School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: lithium-ion batteries; sodium-ion batteries; cathode materials; anode materials; electrochemical

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Guest Editor
School of Material Science and Engineering, Beihang University, Beijing 100191, China
Interests: lithium metal batteries; cathode material; solid-state lithium-ion batteries; electrochemical; lithium-ion batteries
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Special Issue Information

Dear Colleagues,

Electrode materials for lithium-ion and sodium-ion secondary batteries are essential in achieving higher efficiency and reliability in energy storage systems, which are critical in modern technology. For this Special Issue, we invite contributions on novel electrode materials and approaches that drive enhancements in lithium-ion and sodium-ion batteries. We encourage submissions that explore new classes of materials, including nanostructured alloys, high-entropy oxides, transition metal sulfides, and carbon materials. Key focus areas include innovative electrode materials capable of higher energy densities and advanced separator technologies to improve cycle stability. Additionally, we invite studies on interface engineering, ionic conductivity optimization, and degradation resistance, aiming for breakthroughs at the material level to support efficient charge/discharge cycles and extended battery life. We welcome all types of manuscripts, including research articles, reviews, perspectives, and communications, that offer insights into the material-level advancements that are driving the next generation of lithium-ion and sodium-ion rechargeable battery technologies.

Dr. Qi Liu
Dr. Zhikun Zhao
Guest Editors

Manuscript Submission Information

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Keywords

  • lithium-ion batteries
  • sodium-ion batteries
  • cathode materials
  • anode materials
  • solid-state batteries

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Published Papers (1 paper)

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Research

21 pages, 10601 KiB  
Article
Simplified Mechanistic Aging Model for Lithium Ion Batteries in Large-Scale Applications
by Zhe Lv, Huinan Si, Zhe Yang, Jiawen Cui, Zhichao He, Lei Wang, Zhe Li and Jianbo Zhang
Materials 2025, 18(6), 1342; https://doi.org/10.3390/ma18061342 - 18 Mar 2025
Viewed by 303
Abstract
Energy storage systems play a vital role in balancing solar- and wind-generated power. However, the uncertainty of their lifespan is a key factor limiting their large-scale applications. While currently reported battery aging models, empirical or semi-empirical, are capable of accurately assessing battery decay [...] Read more.
Energy storage systems play a vital role in balancing solar- and wind-generated power. However, the uncertainty of their lifespan is a key factor limiting their large-scale applications. While currently reported battery aging models, empirical or semi-empirical, are capable of accurately assessing battery decay under specific operating conditions, they cannot reliably predict the battery lifespan beyond the measured data. Moreover, these models generally require a tedious procedure to determine model parameters, reducing their value for onsite applications. This paper, based on Newman’s pseudo-2D performance model and incorporating microparameters obtained from cell disassembly, developed a mechanistic model accounting for three major aging mechanisms of lithium iron phosphate/graphite cells, i.e., solid electrolyte interphase growth, lithium plating, and gas generation. The prediction of this mechanistic model agrees with the experimental results within an average error of ±1%. The mechanistic model was further simplified into an engineering model consisting of only two core parameters, loss of active lithium and loss of active material, and was more suitable for large-scale applications. The accuracy of the engineering model was validated in a 100 MW/200 MWh energy storage project. When the actual State of Health (SOH) of the battery degraded to 89.78%, the simplified model exhibited an error of −0.17%, and the computation time decreased from 8.12 h to 10 s compared to the mechanistic model. Full article
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