Voltage and Overpotential Prediction of Vanadium Redox Flow Batteries with Artificial Neural Networks

This article explores the novel application of a trained artificial neural network (ANN) in the prediction of vanadium redox flow battery behaviour and compares its performance with that of a two-dimensional numerical model. The aim is to evaluate the capability of two ANNs, one for predicting the cell potential and one for the overpotential under various operating conditions. The two-dimensional model, previously validated with experimental data, was used to generate data to train and test the ANNs. The results show that the first ANN precisely predicts the cell voltage under different states of charge and current density conditions in both the charge and discharge modes. The second ANN, which is responsible for the overpotential calculation, can accurately predict the overpotential across the cell domains, with the lowest confidence near high-gradient areas such as the electrode membrane and domain boundaries. Furthermore, the computational time is substantially reduced, making ANNs a suitable option for the fast understanding and optimisation of VRFBs.This work has been partially supported by the Government of the Basque Country, program: Elkartek CICe2022; Grant No.: KK-2022/00043. U.F.-G. was supported by the Mobility Lab Foundation, a governmental organization of the Provincial Council of Araba and the local council of Vitoria-Gasteiz

Modelling and estimation of vanadium redox flow batteries: a review

Redox flow batteries are one of the most promising technologies for large-scale energy storage, especially in applications based on renewable energies. In this context, considerable efforts have been made in the last few years to overcome the limitations and optimise the performance of this technology, aiming to make it commercially competitive. From the monitoring point of view, one of the biggest challenges is the estimation of the system internal states, such as the state of charge and the state of health, given the complexity of obtaining such information directly from experimental measures. Therefore, many proposals have been recently developed to get rid of such inconvenient measurements and, instead, utilise an algorithm that makes use of a mathematical model in order to rely only on easily measurable variables such as the system’s voltage and current. This review provides a comprehensive study of the different types of dynamic models available in the literature, together with an analysis of the existing model-based estimation strategies. Finally, a discussion about the remaining challenges and possible future research lines on this field is presented.The research that gave rise to these results received support from “la Caixa” Foundation (ID 100010434. Fellowship code LCF/BQ/DI21/11860023) , the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094, CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+ project TRE2103000), the Spanish Ministry of Science and Innovation (project PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033 / ERDF,EU) and the Spanish Ministry of Economy and Competitiveness under Project DOVELAR (ref. RTI2018-096001-B-C32).Peer ReviewedPostprint (published version

Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives

Recently, the appeal of Hybrid Energy Storage Systems (HESSs) has been growing in multiple application fields, such as charging stations, grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance, e.g., efficiency and lifespan. Most recent studies on HESS mainly focus on power management and coupling between the different ESSs without a particular interest in a specific type of ESS. Over the last decades, Redox-Flow Batteries (RFBs) have received significant attention due to their attractive features, especially for stationary storage applications, and hybridization can improve certain characteristics with respect to short-term duration and peak power availability. Presented in this paper is a comprehensive overview of the main concepts of HESSs based on RFBs. Starting with a brief description and a specification of the Key Performance Indicators (KPIs) of common electrochemical storage technologies suitable for hybridization with RFBs, HESS are classified based on battery-oriented and application-oriented KPIs. Furthermore, an optimal coupling architecture of HESS comprising the combination of an RFB and a Supercapacitor (SC) is proposed and evaluated via numerical simulation. Finally, an in-depth study of Energy Management Systems (EMS) is conducted. The general structure of an EMS as well as possible application scenarios are provided to identify commonly used control and optimization parameters. Therefore, the differentiation in system-oriented and application-oriented parameters is applied to literature data. Afterwards, state-of-the-art EMS optimization techniques are discussed. As an optimal EMS is characterized by the prediction of the system’s future behavior and the use of the suitable control technique, a detailed analysis of the previous implemented EMS prediction algorithms and control techniques is carried out. The study summarizes the key aspects and challenges of the electrical hybridization of RFBs and thus gives future perspectives on newly needed optimization and control algorithms for management systems

Energy Storage Management and Simulation for Nano-Grids

Energy storage has been utilized in many forms and applications from a flashlight to the Space Shuttle. There is a worldwide effort to develop battery model with high energy level and power densities for a variety range of applications, including hybrid electric vehicles (HEV) and photovoltaic system (PV). To improve battery technology, understanding the battery modeling is very important. So, modeling the thermal behavior of a battery is a vital consideration before designing an effective thermal management system which will operate safely and prolong the lifespan of an energy storage system. The first part of this work focused on the aging model of lithium-ion battery and a simple thermal model of lithium-ion and lead-acid battery using MATLAB/Simulink. After that, an artificial neural network model (ANN) is developed to predict various characteristics at wide temperature range. In this case, comparisons between the training/testing data outputs and targets validating both models with a regression accuracy of 99.839% and 98.727% respectively for Li-ion and Lead-Acid battery while it is 99.912% for the aging model of Li-ion battery. In the end, this energy storage device is used to interconnect with HOMER. This HOMER project aims at designing a solar-wind hybrid power system for Statesboro, Georgia. The cost analysis is performed utilizing HOMER software based on solar irradiance, wind speed, and residential load profile. The proposed HOMER model, using solar & wind with the grid was more cost efficient as the cost of energy (COE) was found 0.0618$/kWh where the average residential electricity rate in Statesboro is 0.116$/kWh. As a result of using this model, the total cost is reduced by 46.72% compared to other conventional power systems. In the second part of HOMER simulation, while comparing among three types of storage devices, another minimum COE is found using wind with grid connection. As the wind speed is good enough for Statesboro, Georgia, simulation shows that minimum COE is 0.0499$/kWh, 0.0386$/kWh and 0.0633$/kWh respectively for Li-ion, Lead-acid, and Vanadium

Machine learning and simulation for the optimisation and characterisation of electrodes in batterie

The performance of electrochemical energy storage (EES) and energy conversion (EC) technologies is closely related to their electrode microstrcuture. Thus, this work focuses on the development of two novel computational models for the characterisation and optimisation of electrodes for three devices: Redox Flow batteries (RFBs), Solid Oxide Fuel Cells (SOFCs), and Lithium-ion batteries (LIBs). The first method introduces a Pore Network Model (PNM) for simulating the coupled charge and mass transport processes within electrodes. This approach is implemented for a vanadium RFB using different commercially available carbon-based electrodes. The results from the PNM show non-uniformity in the concentration and current density distributions within the electrode, which leads to a fast discharge due to regions where mass-transport limitations are predominant. The second approach is based on the stochastic reconstruction of synthetic electrode microstructures. For this purpose, a deep convolutional generative adversarial network (DC-GAN) is implemented for generating three-dimensional n-phase microstructures of a LIB cathode and a SOFC anode. The results show that the generated data is able to represent the morphological properties and two-point correlation function of the real dataset. As a subsequent process, a generation-optimisation closed-loop algorithm is developed using Gaussian Process Regression and Bayesian optimisation for the design of microstructures with customised properties. The results show the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as an optimisation of these properties constrained by constant values of volume fraction. Overall, this work presents a comprehensive analysis of the effect of the electrode microstructure in the performance of different energy storage devices. The introduction of a PNM bridges the gap between volume-averaged continuum models and detailed the pore-scale models. The main advantage of this model is the ability to visually show the concentration and current distributions inside the electrode within a reasonably low computational time. Based on this, this work represents the first visual showcase of how regions limited by low convective flow affect the rate of discharge in an electrode, which is essential for the design of optimum electrode microstructures. The implementation of DC-GANs allows for the first time the fast generation of arbitrarily large synthetic microstructural volumes of n-phases with realistic properties and with periodic boundaries. The fact that the generator constitutes a virtual representation of the real microstructure allows the inclusion of the generator as a function of the input latent space in a closed-loop optimisation process. For the first time, a set of visually realistic microstructures of a LIB cathode with user-specified morphological properties were designed based on the optimisation of the generator’s latent space. The introduction of a closed-loop generation-optimisation approach represents a breakthrough in the design of optimised electrodes since it constitutes a first approach for evaluating the microstructure-performance correlation in a continuous forward and backward process.Open Acces

Bottom-up design of porous electrodes by combining a genetic algorithm and a pore network model

The microstructure of porous electrodes determines multiple performance-defining properties, such as the available reactive surface area, mass transfer rates, and hydraulic resistance. Thus, optimizing the electrode architecture is a powerful approach to enhance the performance and cost-competitiveness of electrochemical technologies. To expand our current arsenal of electrode materials, we need to build predictive frameworks that can screen a large geometrical design space while being physically representative. Here, we present a novel approach for the optimization of porous electrode microstructures from the bottom-up that couples a genetic algorithm with a previously validated electrochemical pore network model. In this first demonstration, we focus on optimizing redox flow battery electrodes. The genetic algorithm manipulates the pore and throat size distributions of an artificially generated microstructure with fixed pore positions by selecting the best-performing networks, based on the hydraulic and electrochemical performance computed by the model. For the studied VO2+/VO2+ electrolyte, we find an increase in the fitness of 75 % compared to the initial configuration by minimizing the pumping power and maximizing the electrochemical power of the system. The algorithm generates structures with improved fluid distribution through the formation of a bimodal pore size distribution containing preferential longitudinal flow pathways, resulting in a decrease of 73 % for the required pumping power. Furthermore, the optimization yielded an 47 % increase in surface area resulting in an electrochemical performance improvement of 42 %. Our results show the potential of using genetic algorithms combined with pore network models to optimize porous electrode microstructures for a wide range of electrolyte composition and operation conditions.</p

Evaluating the use of a Net-Metering mechanism in microgrids to reducepower generation costs with a swarm-intelligent algorithm

The micro-generation of electricity arises as a clean and efficient alternative to provide electrical power. However, the unpredictability of wind and solar radiation poses a challenge to attend load demand, while maintaining a stable operation of the microgrids (MGs). This paper proposes the modeling and optimization, using a swarm-intelligent algorithm, of a hybrid MG system (HMGS) with a Net-Metering compensation policy. Using real industrial and residential data from a Spanish region, a HMGS with a generic ESS is used to analyze the influence of four different Net-Metering compensation levels regarding costs, percentage of renewable energy sources (RESs), and LOLP. Furthermore, the performance of two ESSs, Lithium Titanate Spinel (Li4Ti5O12 (LTO)) and Vanadium redox flow batteries (VRFB), is assessed in terms of the final $/kWh costs provided by the MG. The results obtained indicate that the Net-Metering policy reduces the surplus from over 14$ dollars of savings, when compared to a MG using a LTO system without Net-Metering.European CommissionAgencia Estatal de InvestigaciónComunidad de Madri

Economic and Energetic Assessment of a Hybrid Vanadium Redox Flow and Lithium-ion batteries considering different Energy Management Strategies

Hybrid energy storage systems (HESS) combine different energy storage technologies aiming at overall system performance and lifetime improvement compared to a single technology system. In this work, control combinations for a vanadium redox flow battery (VRFB, 5/60 kW/kWh) and a lithium-ion battery (LIB, 3.3/9.8 kW/kWh) are investigated for the design of a HESS. Four real-time energy management/power allocation scenarios are considered for the operation of the hybrid storage solution through a 15-year economic and energetic analysis. The results obtained for each scenario are compared with a single technology battery performance. In the definition of the scenarios, real electricity generation is considered from two solar photovoltaic installations (3.2 kWp and 6.7 kWp) and an estimated representative load of a services building. HESS performance is evaluated through specific energy and economic key performance indicators. The results obtained indicate that the use of customized energy management strategies (EMSs) renders the VRFB and LIB characteristics complementary, besides enhancing the competitiveness of VRFB, as a single technology. Moreover, the HESS management impacts the seasonality factor, contributing to the overall electric system smart management.Comment: 19 pages, 7 figures, research pape