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Batteries
Special Issues
Recent Progress of Flow Battery
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Recent Progress of Flow Battery

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 5076

Special Issue Editor

Prof. Dr. Puiki Leung

E-Mail Website
Guest Editor
School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China
Interests: energy storage; organic batteries; electrochemical reduction of CO2

Special Issue Information

Dear Colleagues,

Redox flow battery (RFB) is one of the most promising technologies for grid-scale stationary energy storage, due to its design flexibility in decoupling power and energy, long life-time, high safety, and low environmental impact. In recent years, this technology has received significant attention and successfully been scaled up to MW scale. To ensure effective market penetration, new chemistries based on low-cost materials or with improved energy densities have recently been introduced in aqueous and non-aqueous electrolytes. This Special Issue will focus on the latest advances and prospects of current and future flow battery systems, covering key topics in new chemistries, functional materials, engineering, cost/market and computational modelling.

Topics of interest include, but are not limited to:

  • Redox couples/battery chemistries;
  • Functional materials (e.g., electrodes and membranes);
  • Engineering (e.g., scale-up, new cell structures/designs);
  • Mass transport phenomena;
  • Operations (diagnostics and management);
  • Cost and market (e.g., life-cycle assessments);
  • Modelling and simulations (e.g., multi-physics models, first-principles calculations).

Prof. Dr. Puiki Leung
Guest Editor

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. Batteries is an international peer-reviewed open access monthly 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 2700 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

20 pages, 1152 KiB  
Article
Optimization of a Redox-Flow Battery Simulation Model Based on a Deep Reinforcement Learning Approach
by Mariem Ben Ahmed and Wiem Fekih Hassen
Batteries 2024, 10(1), 8; https://doi.org/10.3390/batteries10010008 - 26 Dec 2023
Cited by 1 | Viewed by 2232
Abstract
Vanadium redox-flow batteries (VRFBs) have played a significant role in hybrid energy storage systems (HESSs) over the last few decades owing to their unique characteristics and advantages. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as [...] Read more.
Vanadium redox-flow batteries (VRFBs) have played a significant role in hybrid energy storage systems (HESSs) over the last few decades owing to their unique characteristics and advantages. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as they are indispensable for incorporating the distinctive features of energy storage systems and control algorithms within embedded energy architectures. In this work, we propose a novel approach that combines model-based and data-driven techniques to predict battery state variables, i.e., the state of charge (SoC), voltage, and current. Our proposal leverages enhanced deep reinforcement learning techniques, specifically deep q-learning (DQN), by combining q-learning with neural networks to optimize the VRFB-specific parameters, ensuring a robust fit between the real and simulated data. Our proposed method outperforms the existing approach in voltage prediction. Subsequently, we enhance the proposed approach by incorporating a second deep RL algorithm—dueling DQN—which is an improvement of DQN, resulting in a 10% improvement in the results, especially in terms of voltage prediction. The proposed approach results in an accurate VFRB model that can be generalized to several types of redox-flow batteries. Full article
(This article belongs to the Special Issue Recent Progress of Flow Battery)
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16 pages, 3581 KiB  
Article
Exploring the Performance and Mass-Transfer Characteristics of Porous Zinc Anodes for Membraneless Hybrid-Flow Batteries
by Lina Tang, Shuyang Dai, Puiki Leung, Mohd Rusllim Mohamed, Yikai Zeng, Xun Zhu, Cristina Flox, Akeel A. Shah and Qiang Liao
Batteries 2023, 9(7), 340; https://doi.org/10.3390/batteries9070340 - 22 Jun 2023
Cited by 3 | Viewed by 1890
Abstract
Zinc-based hybrid-flow batteries are considered as a promising alternative to conventional electrochemical energy-storage systems for medium- to large-scale applications due to their high energy densities, safety, and abundance. However, the performance of these batteries has been limited by issues such as dendritic growth [...] Read more.
Zinc-based hybrid-flow batteries are considered as a promising alternative to conventional electrochemical energy-storage systems for medium- to large-scale applications due to their high energy densities, safety, and abundance. However, the performance of these batteries has been limited by issues such as dendritic growth and passivation of zinc anodes during charge–discharge cycling. To address this challenge, a variety of two- and three-dimensional zinc anodes have been investigated. While two-dimensional zinc anodes have been extensively studied, there has been limited investigation into three-dimensional zinc anodes for hybrid-flow batteries. This study highlights the potential of three-dimensional zinc anodes to mitigate overpotentials and improve the mass transport of active species to promote negative electrode reactions. The performance of a membraneless flow battery based on low-cost zinc and organic quinone was herein evaluated using experimental and numerical approaches. Specifically, the use of zinc fiber was shown to yield an average coulombic efficiency of approximately 90% and an average voltage efficiency of approximately 82% over the course of 100 cycles at a current density of 30 mA cm−2. These results indicate the viability of using zinc fiber anodes to improve the performance of existing hybrid-flow batteries. Full article
(This article belongs to the Special Issue Recent Progress of Flow Battery)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Optimization of a Redox Flow Battery Simulation Model based on a Deep Reinforcement Learning Approach
Authors: Mariam Ben Ahmed; Wiem Fekih Hassen
Affiliation: University of Passau
Abstract: Vanadium Redox-Flow Batteries (VRFBs) play a significant role in Hybrid Energy Storage Systems (HESSs) due to their unique characteristics and advantages during the last decades. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as they are indispensable for incorporating the distinctive features of Energy Storage Systems (ESSs) and control algorithms within embedded energy architectures. In this work, we propose a novel approach that combines model-based and data-driven techniques to predict battery state variables, i.e., the State of Charge (SoC), the Voltage, and the Current. Our proposal leverages enhanced Deep Reinforcement Learning techniques, specifically Deep QLearning (DQN), combining Q-Learning with Neural Networks to optimize the VRFB-specific parameters, ensuring a robust fit between the real and simulated data. Our proposed method outperforms the existing approach in voltage prediction. Subsequently, we enhance the proposed approach by incorporating a second deep RL algorithm, Dueling DQN which is an improvement of the DQN, resulting in a 10% improvement in the results, especially for the Voltage prediction. The proposed approach results in an accurate model for VFRB and can be generalized to several types of Redox Flow Batteries (RFB).

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