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Applied Sciences
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Emerging Lithium‐Ion Batteries: Design, Characterization, and Applications
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Emerging Lithium‐Ion Batteries: Design, Characterization, and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Green Sustainable Science and Technology".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1078

Special Issue Editors

Dr. Davide Clerici

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: lithium-ion battery modelling; mechanics of lithium-ion batteries; lithium-ion battery diagnostics; degradation modelling of lithium-ion batteries
Dr. Francesco Mocera

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: design of innovative hybrid and electric powertrains for working machines in the agri-construction sector; study of electro-mechanical performance of lithium-ion batteries
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are increasingly used in diverse applications and will play a decisive role in achieving a more sustainable and efficient energy transition; therefore, this Special Issue seeks to provide technical in-depth analysis of the latest advancements in lithium-ion batteries, focusing on the interplay between materials science and electrochemical, electrical, and mechanical engineering.

The main objective of this Special Issue is to disseminate recent research efforts contributing to advances in lithium-ion battery design and characterization, as well as to showcase innovative applications.

Consequently, we welcome contributions exploring innovative electrode materials such as high-capacity silicon anodes and high-voltage cathodes, alongside novel electrolyte formulations designed to enhance ionic conductivity and stability under extreme conditions.

Engineering challenges, such as minimizing interfacial resistance, managing thermal effects, and improving charge/discharge kinetics, are also welcome.

Finally, studies on novel cell architectures and design are encouraged, including solid-state and hybrid configurations, which promise breakthroughs in energy density, longevity, and safety.

From the perspective of characterization, advanced characterization methods, including operando impedance spectroscopy, in situ electron microscopy, and computed tomography techniques, are highlighted for their role in elucidating electrochemical mechanisms, detecting failure modes, and guiding material optimization at the atomic level.

Computational physics-based modelling and machine learning approaches are also welcome as tools to accelerate material discovery and predict battery health and remaining useful life under various operating scenarios.

Finally, practical applications will be discussed with a focus on battery integration into high-power systems like electric vehicles and grid-scale storage. Finally, we also hope to attract studies on recycling technologies and lifecycle analysis aiming to ensure the sustainable scaling of the lithium-ion battery technology.

Dr. Davide Clerici
Dr. Francesco Mocera
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lithium-ion cell design
  • computation modelling of lithium-ion batteries
  • experimental testing for lithium-ion batteries
  • mechanics of lithium-ion batteries
  • EIS
  • innovative applications for lithium-ion batteries
  • in situ testing
  • SOH estimation
  • remaining useful life estimation
  • AI modelling of lithium-ion batteries

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Published Papers (4 papers)

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Research

15 pages, 3437 KiB  
Article
Unveiling State-of-Charge Effects on Elastic Properties of LiCoO2 via Deep Learning and Empirical Models
by Ijaz Ul Haq and Seungjun Lee
Appl. Sci. 2025, 15(14), 7809; https://doi.org/10.3390/app15147809 - 11 Jul 2025
Viewed by 28
Abstract
This study investigates the mechanical properties of LiCoO2 (LCO) cathode materials under varying states of charge (SOCs) using both an empirical Buckingham potential model and a machine learning-based Deep Potential (DP) model. The results reveal a substantial decrease in Young’s modulus with [...] Read more.
This study investigates the mechanical properties of LiCoO2 (LCO) cathode materials under varying states of charge (SOCs) using both an empirical Buckingham potential model and a machine learning-based Deep Potential (DP) model. The results reveal a substantial decrease in Young’s modulus with decreasing SOC. Analysis of stress factors identified pairwise interactions, particularly those involving Co3+ and Co4+, as key drivers of this mechanical evolution. The DP model demonstrated superior performance by providing consistent and reliable predictions reflected in a smooth and monotonic stiffness decrease with SOC, in contrast to the large fluctuations observed in the classical Buckingham potential results. The study further identifies the increasing dominance of Co4+ interactions at low SOCs as a contributor to localized stress concentrations, which may accelerate crack initiation and mechanical degradation. These findings underscore the DP model’s capability to capture SOC-dependent mechanical behavior accurately, establishing it as a robust tool for modeling battery materials. Moreover, the calculated SOC-dependent mechanical properties can serve as critical input for continuum-scale models, improving their predictive capability for chemo-mechanical behavior and degradation processes. This integrated multiscale modeling approach can offer valuable insights for developing strategies to enhance the durability and performance of lithium-ion battery materials. Full article
(This article belongs to the Special Issue Emerging Lithium‐Ion Batteries: Design, Characterization, and Applications)
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20 pages, 2464 KiB  
Article
Improved Electrochemical–Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
by Salvatore Scalzo, Davide Clerici, Francesca Pistorio and Aurelio Somà
Appl. Sci. 2025, 15(13), 7217; https://doi.org/10.3390/app15137217 - 26 Jun 2025
Viewed by 220
Abstract
Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. The reliability of these models depends on the accurate estimation of internal electrochemical and mechanical parameters, many of which are not directly measurable and must be [...] Read more.
Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. The reliability of these models depends on the accurate estimation of internal electrochemical and mechanical parameters, many of which are not directly measurable and must be identified via model-based fitting of experimental data. Unlike other parameter-estimation procedures, this study introduces a novel approach that integrates mechanical measurements with electrical data, with a specific application for lithium iron phosphate (LFP) cells. An error analysis—based on the Root Mean Square Error (RMSE) and confidence ellipses—confirms that the inclusion of mechanical measurements significantly improves the accuracy of the identified parameters and the reliability of the algorithm compared to approaches relying just on electrochemical data. Two scenarios are analyzed: in the first, a teardown of the cell provides direct measurements of electrode thicknesses and the number of layers; in the second, these values are treated as additional unknown parameters. In the teardown case, the electrochemical–mechanical approach achieves significantly lower RMSEs and smaller confidence ellipses, proving its superior accuracy and consistency. In the second scenario, while the RMSE values of electrochemical-mechanical model are similar to those of the purely electrochemical one, the smaller ellipses still indicate better consistency and convergence in the parameter estimates. Furthermore, a sensitivity analysis to initial guesses shows that the electrochemical-mechanical approach is more stable, consistently converging to coherent parameter values and confirming its greater reliability. Full article
(This article belongs to the Special Issue Emerging Lithium‐Ion Batteries: Design, Characterization, and Applications)
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15 pages, 1787 KiB  
Article
Probing Solid-State Interface Kinetics via Alternating Current Electrophoretic Deposition: LiFePO4 Li-Metal Batteries
by Su Jeong Lee and Byoungnam Park
Appl. Sci. 2025, 15(13), 7120; https://doi.org/10.3390/app15137120 - 24 Jun 2025
Viewed by 235
Abstract
This work presents a comprehensive investigation into the interfacial charge storage mechanisms and lithium-ion transport behavior of Li-metal all-solid-state batteries (ASSBs) employing LiFePO4 (LFP) cathodes fabricated via alternating current electrophoretic deposition (AC-EPD) and Li1.3Al0.3Ti1.7(PO4) [...] Read more.
This work presents a comprehensive investigation into the interfacial charge storage mechanisms and lithium-ion transport behavior of Li-metal all-solid-state batteries (ASSBs) employing LiFePO4 (LFP) cathodes fabricated via alternating current electrophoretic deposition (AC-EPD) and Li1.3Al0.3Ti1.7(PO4)3 (LATP) as the solid-state electrolyte. We demonstrate that optimal sintering improves the LATP–LFP interfacial contact, leading to higher lithium diffusivity (~10−9 cm2∙s−1) and diffusion-controlled kinetics (b ≈ 0.5), which directly translate to better rate capability. Structural and electrochemical analyses—including X-ray diffraction, scanning electron microscopy, cyclic voltammetry, and rate capability tests—demonstrate that the cell with LATP sintered at 900 °C delivers the highest Li-ion diffusivity (~10−9 cm2∙s−1), near-ideal diffusion-controlled behavior (b-values ~0.5), and superior rate capability. In contrast, excessive sintering at 1000 °C led to reduced diffusivity (~10−10 cm2∙s−1). The liquid electrolyte system showed higher b-values (~0.58), indicating the inclusion of surface capacitive behavior. The correlation between b-values, diffusivity, and morphology underscores the critical role of interface engineering and electrolyte processing in determining the performance of solid-state batteries. This study establishes AC-EPD as a viable and scalable method for fabricating additive-free LFP cathodes and offers new insights into the structure–property relationships governing the interfacial transport in ASSBs. Full article
(This article belongs to the Special Issue Emerging Lithium‐Ion Batteries: Design, Characterization, and Applications)
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13 pages, 2272 KiB  
Article
Performance Enhancement of Second-Life Lithium-Ion Batteries Based on Gaussian Mixture Model Clustering and Simulation-Based Evaluation for Energy Storage System Applications
by Abdul Shakoor Akram and Woojin Choi
Appl. Sci. 2025, 15(12), 6787; https://doi.org/10.3390/app15126787 - 17 Jun 2025
Viewed by 282
Abstract
Lithium-ion batteries (LIBs) are widely deployed in electric vehicles due to their high energy density and long cycle life. Even after retirement, typically at around 80% of their rated capacity, LIBs can still be repurposed for second-life applications such as residential energy storage [...] Read more.
Lithium-ion batteries (LIBs) are widely deployed in electric vehicles due to their high energy density and long cycle life. Even after retirement, typically at around 80% of their rated capacity, LIBs can still be repurposed for second-life applications such as residential energy storage systems (ESSs). However, effectively grouping these heterogeneous cells is crucial to optimizing performance of the module. Retired LIBs can be effectively repurposed for numerous second-life applications such as ESSs, and other power backups. In this paper, we compare four clustering approaches including random grouping, equal-number Support Vector Clustering, K-means, and an equal-number Gaussian Mixture Model (GMM) to organize 60 retired cells into 48 V modules consisting of 15-cell groups. We verify the performance of each method via simulations of a 15S2P configuration, focusing on the standard deviation of final charge voltage, average charge throughput, delta capacity, and coulombic efficiency. Based on the evaluation metrics analyzed after regrouping the battery cells and simulating them for second-life ESS applications, the GMM-based clustering method demonstrates better performance. Full article
(This article belongs to the Special Issue Emerging Lithium‐Ion Batteries: Design, Characterization, and Applications)
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