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Optimization of a Redox-Flow Battery Simulation Model Based on a Deep Reinforcement Learning Approach

  • 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 secondVanadium 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.show moreshow less

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Metadaten
Author:Wiem Fekih HassenORCiD, Mariem Ben Ahmed
URN:urn:nbn:de:bvb:739-opus4-13994
DOI:https://doi.org/10.3390/batteries10010008
Parent Title (English):Batteries
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of first Publication:2023/12/26
Publishing Institution:Universität Passau
Release Date:2024/03/13
Tag:accurate estimation; battery modeling; battery state variables; deep q-learning; deep reinforcement learning; dueling deep q-networks; energy storage; parameter optimization; redox-flow battery; voltage prediction
Volume:10
Page Number:20 Seiten
Institutes:Fakultät für Informatik und Mathematik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
open_access (DINI-Set):open_access
Funding Acknowledgement:Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International