Recent Progress of Flow Battery

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.

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).