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Energies
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Electrochemical Conversion and Energy Storage System
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Electrochemical Conversion and Energy Storage System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: 18 September 2024 | Viewed by 2127

Special Issue Editors

Dr. Zubair Ahmad

E-Mail Website
Guest Editor
School of Chemical Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Republic of Korea
Interests: supercapacitors; polymer-based energy storage systems; electrochemical energy systems; energy conversion
Dr. Sachin Kumar

E-Mail Website
Guest Editor
School of Chemical Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Republic of Korea
Interests: batteries; supercapacitor
Prof. Dr. Jae-Suk Lee

E-Mail Website
Guest Editor
School of Materials Science and Engineering , Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Republic of Korea
Interests: nanotechnology; materials chemistry; polymer chemistry; energy storage systems
Dr. Mohd Zahid Ansari

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Republic of Korea
Interests: 2D materials, thin films; nanomaterials; electrodes materials; rechargeable batteries; supercapacitors; catalyst;

Special Issue Information

Dear Colleagues,

Electrochemical conversion and energy storage systems play vital roles in addressing the increasing demand for sustainable energy solutions. These systems encompass a wide range of technologies that enable efficient energy conversion, storage, and utilization.

Electrochemical conversion involves the transformation of chemical energy into electrical energy, or vice versa, through redox reactions. One example can be seen with fuel cells, which are capable of converting the chemical energy of fuels, such as hydrogen, into electricity with high efficiency and minimal environmental impact. Another example is electrolyzers, which use electricity to split water into hydrogen and oxygen for energy storage or the production of clean fuels.

Electrochemical capacitors, commonly known as supercapacitors, provide high power density and possess fast charging/discharging capabilities. They are ideal for applications requiring quick bursts of energy, such as regenerative braking in vehicles and smoothing out power fluctuations in renewable energy systems.

This Special Issue aims to publish high-quality research and review papers related to electrochemical conversion and energy storage systems. Topics of interest for publication include, but are not limited to:

  • Advances in electrode materials;
  • Electrochemical energy storage technologies;
  • Electrolysis and fuel cells;
  • Electrochemical energy conversion for renewable energy integration.

Dr. Zubair Ahmad
Dr. Sachin Kumar
Prof. Dr. Jae-Suk Lee
Dr. Mohd Zahid Ansari
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. Energies 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 2600 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

  • energy storage
  • batteries
  • fuel cells
  • electrochemical reactors
  • energy
  • electrochemical energy systems
  • energy conversion

Published Papers (2 papers)

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Research

11 pages, 9419 KiB  
Article
Finite Element Analysis of the Mechanical Response for Cylindrical Lithium-Ion Batteries with the Double-Layer Windings
by Young Ju Ahn
Energies 2024, 17(14), 3357; https://doi.org/10.3390/en17143357 (registering DOI) - 9 Jul 2024
Viewed by 371
Abstract
The plastic properties for the jellyroll of lithium-ion batteries showed different behavior in tension and compression, showing the yield strength in compression being several times higher than in tension. The crushable foam models were widely used to predict the mechanical responses to compressive [...] Read more.
The plastic properties for the jellyroll of lithium-ion batteries showed different behavior in tension and compression, showing the yield strength in compression being several times higher than in tension. The crushable foam models were widely used to predict the mechanical responses to compressive loadings. However, since the compressive characteristic is dominant in this model, it is difficult to identify distributions of the yield strength in tension. In this study, a simplified jellyroll model consisting of double-layer windings was devised to reflect different plastic characteristics of a jellyroll, and the proposed model was applied to an 18650 cylindrical battery under compressive loading conditions. One winding adopted the crushable foam model for representing the compressive plastic behavior, and the other winding adopted the elastoplastic models for tracking the tensile plastic behavior. The material parameters in the crushable foam model were calibrated by comparing the simulated force–displacement curve with the experimental one for the case where the cell was crushed between two plates when the punch was displaced by 7 mm. A specific cut-off value (10 MPa) was assigned to a yield stress limit in the elastoplastic model. Further, the computational model was validated with two more loading cases, a cylindrical rod indentation and a spherical punch indentation, as the punch was displaced by 6.3 mm and 6.5 mm, respectively. For three loading cases, deformed configurations and plastic strain distributions were investigated by finite element analysis. It was found that the proposed model clearly provides the plastic behavior both in compression and tension. For the crush simulation, the maximum compressive stress approached 222 MPa in the middle of the jellyroll, and the maximum effective plastic strain approached 60% in the middle of the layered roll. For indentation with the cylindrical and the spherical punch, the maximum effective plastic strain approached 52% and 277% in the layered roll, respectively. The local crack or location of a short circuit could be predicted from the maximum effective plastic strain. Full article
(This article belongs to the Special Issue Electrochemical Conversion and Energy Storage System)
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22 pages, 5451 KiB  
Article
Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging
by Sercan Yalçın and Münür Sacit Herdem
Energies 2024, 17(12), 2883; https://doi.org/10.3390/en17122883 - 12 Jun 2024
Viewed by 570
Abstract
This paper investigates the application of hybrid reinforcement learning (RL) models to optimize lithium-ion batteries’ charging and discharging processes in electric vehicles (EVs). By integrating two advanced RL algorithms—deep Q-learning (DQL) and active-critic learning—within the framework of battery management systems (BMSs), this study [...] Read more.
This paper investigates the application of hybrid reinforcement learning (RL) models to optimize lithium-ion batteries’ charging and discharging processes in electric vehicles (EVs). By integrating two advanced RL algorithms—deep Q-learning (DQL) and active-critic learning—within the framework of battery management systems (BMSs), this study aims to harness the combined strengths of these techniques to improve battery efficiency, performance, and lifespan. The hybrid models are put through their paces via simulation and experimental validation, demonstrating their capability to devise optimal battery management strategies. These strategies effectively adapt to variations in battery state of health (SOH) and state of charge (SOC) relative error, combat battery voltage aging, and adhere to complex operational constraints, including charging/discharging schedules. The results underscore the potential of RL-based hybrid models to enhance BMSs in EVs, offering tangible contributions towards more sustainable and reliable electric transportation systems. Full article
(This article belongs to the Special Issue Electrochemical Conversion and Energy Storage System)
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