Real Battery Research Articles

Selective recovery of lithium and ammonium from spent lithium-ion batteries using intercalation electrodes

Recycling lithium-ion batteries has recently become a major concern. Ammonia leaching is commonly employed in such battery recycling methods since it has various advantages such as low toxicity and excellent selectivity toward precious metals. In this study, an electrochemical system with intercalation-type electrodes was used to investigate the selective recovery of lithium and ammonium from ammonia battery leachate. Using an activated carbon electrode as a counter electrode, the selectivity of lithium from the lithium manganese oxide (LMO) electrode and the selectivity of ammonium from the nickel hexacyanoferrate (NiHCF) electrode were examined within the system. The LMO//NiHCF system was next evaluated for lithium and ammonium recovery using a synthetic solution as well as real ammonia battery leachate. When compared to previous ammonium recovery methods, the results revealed good selectivity of lithium and ammonium from each LMO and NiHCF electrode with relatively low energy consumption for ammonium recovery (2.43 Wh g-N−1). The average recovery capacity of lithium was 1.39 mmol g−1 with a purity of up to 96.8% and the recovery capacity of ammonium was 1.09 mmol g−1 with 97.8% purity from the pre-treated leachate. This electrochemical method together with ammonia leaching can be a promising method for selective resource recovery from spent lithium-ion batteries.

Temporal Resolution of Input Weather Data Strongly Affects an Off-Grid PV System Layout and Reliability

Renewable energy systems design using average year weather data is a standard approach that works well for grid-tied systems, but for stand-alone ones, it leads to dramatic mistakes. We considered the effect of meteorological data temporal resolution (5, 10, 15, 20, 30 min; 1, 2, 3, 4 h) on a stand-alone hybrid system’s layout in terms of equipment cost, power supply reliability and maximum duration of interruption for monitoring equipment in the Alps. We have shown that lifecycle costs could be strongly (order of magnitude) underestimated for off-grid systems, as well as their reliability overestimated. Lower temporal resolution data lead to the underestimation of energy storage charge–discharge cycles (considering depth of discharge too)—real batteries are to be replaced more often, which matches our practical experience as well. Even a 5 to 10 min decrease in weather data temporal resolution leads to the estimated annual expenses being halved. In general, we recommend using 30 min. resolution.

NEXAFS spectra of model sulfide chains: implications for sulfur networks obtained from inverse vulcanization.

Inverse vulcanization is a promising route to stabilize sulfur in lithium-sulfur batteries, but the resulting sulfur strand lengths in the materials are elusive. We address the strand length by characterization via sulfur near edge X-ray absorption fine structure (NEXAFS) spectroscopy. Theoretical predictions of NEXAFS spectra for model molecules containing strands with up to three sulfur atoms are verified by experiment. The near perfect agreement between simulation and experiment on the absolute energy scale allows for the predictions for larger chain lengths also. Inspection and interpretation of NEXAFS spectra from real battery materials on this basis reveals the appearance of single connecting sulfur atoms for very low sulfur content, and of longer strands when the sulfur fraction increases.

Knowledge-Guided Data-Driven Model With Transfer Concept for Battery Calendar Ageing Trajectory Prediction

Dear Editor, Lithium-ion (Li-ion) battery has become a promising source to supply and absorb energy/power for many energy-transportation applications. However, Li-ion battery capacity would inevitably degrade over time, making its related ageing prediction necessary. This letter presents effective battery calendar ageing trajectory prediction by deriving a knowledge-guided data-driven model with transfer concept. More specifically, this data-driven model is based on the support vector regression (SVR) technology. To ensure highly-accurate prognostics of battery calendar ageing trajectory under wit-nessed conditions, a knowledge-guided kernel is first developed by coupling the mechanism and empirical knowledge elements of battery storage temperature, state-of-charge (SoC), and time. To im-prove model's generalization ability under unwitnessed conditions, the knowledge-guided data-driven model is then equipped with trans-fer concept by adding a classical Gaussian kernel for all inputs. A well-rounded real battery ageing dataset under eight different storage conditions is collected to evaluate the performance of developed model. Results illustrate that this knowledge-guided battery ageing trajectory prediction model presents satisfactory accuracy for wit-nessed conditions with R2 over 0.98. After using only 20% starting capacity point to tune its transfer part, it can also generalize well for unwitnessed conditions with R2 over 0.97, further heavily reducing the required ageing experimental time and cost.

Deciphering the effects of electrolyte concentration on the performance of lithium batteries by correlative surface characterization.

The formation of solid electrolyte interphase (SEI) and lithium ion intercalation are two crucial processes in lithium ion batteries. Given the complexity and challenges involved in investigating real batteries, a combination of model batteries comprising well-defined ultrathin graphite electrodes with surface-sensitive techniques can provide valuable information on these processes. Here, a comparative study of the performance of batteries using regular- and high-concentration electrolytes (RCE and HCE) is presented, aided by a myriad of correlative surface science techniques. It is found that the SEI formed in HCE possesses a thinner organic layer and more inorganic substances. Such an effective SEI protects the graphite electrode and facilitates the processes of Li+ intercalation/deintercalation, thereby improving the reversibility and cycling stability of the battery, superior to those with RCE. Moreover, the battery performance can be readily tuned by switching the electrolytes. The good cycling stability of the battery can still be maintained in RCE after the preformation of a robust SEI on the ultrathin graphite electrode in HCE. This work highlights the importance of electrolyte-modulated SEI formation to battery performance and provides powerful platforms to comprehensively study the SEI formation and ion intercalation processes.

A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries

Recently, there has been a significant growth in the development of rechargeable battery-powered devices such as electric vehicles, leading to an urgent need for reliable and safe batteries. The remaining useful life (RUL) is a critical health indicator of battery, which is defined as the remaining number of charge and recharge cycles before the state-of-health falls below a user-specified threshold under certain operating settings. Substantially, the RUL can be estimated by adaptive stochastic processes or advanced machine learning techniques. However, the existing approaches either assume over-simplified degradation pattern in accordance with physics laws leading to poor generalizability or act as a black box offering no interpretation. To address these limitations, in this article, we develop a pattern-driven degradation process by integrating a recursive Gaussian distribution with its mean learnt from a gated recurrent unit (GRU) driven degradation pattern to capture degradation fluctuation into the model. Due to the non-Markovian state transitions, a joint-learning sampling-based expectation maximization algorithm was developed to estimate model parameters based on historical observations. Finally, numerical studies using real battery data showed that the proposed method achieves over 3% and 40% higher accuracy in RUL prediction than the GRU and adaptive Wiener process, respectively.

Prediction on thermal performance of refrigerant-based battery thermal management system for a HEV battery pack

The thermal performance of refrigerant-based battery thermal management system (BTMS) for a real HEV battery pack in both the cooling and heating conditions is numerically and experimentally investigated in this paper. The heat generation model of battery cells and the numerical methodology for predicting the cooling performance of refrigerant-based cooling system are validated by experiments and the predicted results are consistent with experimental data. The effects of discharging C-rate, mass flow rate, initial temperature and vapor quality of refrigerant on thermal performance of battery pack are analyzed in detail. The thermal performance of refrigerant-based BTMS at a real driving cycle is also examined. The results indicate that the discharging C-rate has a significant impact on thermal performance of battery pack due to the distinctive heat transfer behaviors of battery pack at different discharging C-rates. Increasing mass flow rate of refrigerant is contributed to descend the maximum temperature and improve temperature uniformity of battery pack. Decreasing initial temperature of refrigerant is beneficial to reducing the maximum temperature of battery pack but is disadvantageous to temperature uniformity. The effect of vapor quality on thermal performance of battery pack is limited. The cooling performance of refrigerant satisfies the requirement of BTMS at the real driving cycle. The optimum heating power of the battery pack at heating condition ranges from 400 W to 460 W.

Augmented Random Search with Artificial Neural Networks for energy cost optimization with battery control

Intermittent renewable energy production and dynamic load must be balanced through appropriate control of integrated energy storage to account for the temporal discrepancy among power supply and demand. Intelligent control systems are required to anticipate and optimize the charging and discharging of energy storage. In recent years, reinforcement learning based techniques have been applied to a multitude of problems, including building integrated energy storage solutions. In this work, the focus is on the application of reinforcement learning based techniques to the specific energy optimization problem of controlling a battery energy storage system in a smart warehouse. This paper adopts data from a real operational battery energy storage system installed in a smart warehouse, integrated with photovoltaic, for food distribution on the west coast of Norway. In the smart warehouse, an intelligent energy management system controls the on-site battery energy storage using machine learning predictions of load and photovoltaic production, and an optimization algorithm is presented to generate a schedule for effective utilization of battery energy storage in coordination with a thermal storage system. This paper presents the combination of the augmented random search reinforcement algorithm with artificial neural networks as a basis to design an intelligent energy management system for controlling energy flows of battery energy storage systems to minimize the energy cost. The developed algorithm finds very promising solutions in the considered case-study of a smart house for energy cost minimization through a battery energy storage system, achieving an average of 99.2% accuracy across 10 seeded trials.

Investigating the Charge and Discharge Mechanisms for Lithium-Sulfur Batteries Using Electrochemical Impedance Spectroscopy

The advent of electric vehicles, grid storage, and more powerful electronic devices has made it necessary to develop a new generation of high energy rechargeable batteries to overcome the inadequacies of the current lithium-ion batteries (LIB) technology. Replacing the conventional cathode material with Sulfur and pairing it with a Lithium (Li) metal anode to obtain the Lithium-Sulfur (Li-S) battery is a promising approach. Li-S has a theoretical specific energy density of 2600 Wh/kg, which is significantly higher than what the modern LIB can offer1. In addition, the low cost, natural abundance, and environmental friendliness of sulfur suit commercial consideration.However, the path to commercialization and adoption of Li-S has several challenges. One of the major challenges in Li-S batteries relates to the undesired solubility of the sulfur products in the liquid electrolyte, resulting in so-called Li polysulfides (LiPSs) “shuttling”. During the discharge, the reaction of Li and sulfur results in the stepwise transformation of stable ring-shaped sulfur (S8) to a series of LiPSs (Li2Sx, x = 1, 2, 4, 6, 8). Unfortunately, the long-chain LiPSs have high solubility in the electrolyte that facilitates the back-and-forth transport of the LiPSs between the electrodes. The result is a “chemical short” in the cell, a loss of active material, and poor cyclability2. To overcome this problem, a clear understanding of the discharge and charge electrochemical reactions in Li-S battery is much needed.Electrochemical Impedance Spectroscopy (EIS) is a powerful technique to study the electrochemical mechanisms at work in many technologies, including the Li-S battery. Because of this, multiple EIS-based measurements for the Li-S battery have been reported. However, those attempts carried out EIS measurements during the first cycle3–5 or the first few cycles6, which does not provide robust insight into the conditioned charge and discharge processes taking place in a real battery as it operates for many cycles. Hence, a clear understanding of the electrochemical processes and their mechanisms in Li-S batteries at extended cycle numbers is needed.In this work, we expand our understanding of the Li-S charge and discharge mechanisms at a high number of cycles and various depths of discharge (DoD) and states of charge (SoC). An equivalent circuit model of the EIS spectra was proposed from the Nyquist plots. New insights about the individual contribution of charge transfer resistance and the impedance resulting from the deposition of non-conducting Li2S/Li2S2 at high number of cycles were achieved. These new insights are expected to provide a better understanding of the Li-S performance during extended cycle life. References S. Evers and L. F. Nazar, Acc. Chem. Res., 46, 1135–1143 (2013).A. Manthiram, Y. Fu, S.-H. Chung, C. Zu, and Y.-S. Su, Chem. Rev., 114, 11751–11787 (2014).S. Waluś, C. Barchasz, R. Bouchet, and F. Alloin, Electrochimica Acta, 359, 136944 (2020).N. A. Cañas et al., Electrochimica Acta, 97, 42–51 (2013).L. Yuan, X. Qiu, L. Chen, and W. Zhu, J. Power Sources, 189, 127–132 (2009).V. Kolosnitsyn, E. V. Kuz’mina, E. Karaseva, and S. E. Mochalov, J. Power Sources, 196, 1478–1482 (2011).

Data-Driven Direct Diagnosis of PV Connected Batteries

Photovoltaic systems are providing a growing share of power to the electric grid worldwide. As more and more of these systems are tied with battery energy storage to balance their intermittency, being able to ensure long and safe operation of these batteries will become critical. Because of their sporadic usage, far from the typical constant current or constant power, diagnosability without having to perform lengthy maintenance cycles will be a key factor to accelerate deployment.This work proposes a new methodology to enable the training of machine learning algorithms so they could be applied directly to photovoltaic (PV) battery charging data for opportunistic diagnosis. This is done by training the algorithms on synthetic voltage data under different degradations calculated from clear-sky model irradiance data. Validation was performed on synthetic voltage responses calculated from plane of array irradiance observations for a PV system located in Maui, HI, USA.Herein, we will present the result of our proof-of-concept study where we investigated the diagnosability of the voltage response of PV charged batteries under 18 different cloud coverages scenarios and for more than 10,000 different degradations. We also evaluated the effect of training conditions on clear-sky irradiance data by analyzing the resulting differences from training on 700,000 different voltage curves for a single day vs. 45,000 different voltage curves for of each of the 18 days used for cloud coverage estimation. In addition, we considered and quantified the effect of cell-to-cell variations. Training was performed on five state-of-the-art machine learning algorithms for battery diagnosis which included decision tree ensemble methods and neural networks. Finally, because the diagnosis was done outside of constant current, the capacity- and time-based information were decorrelated and compared. While time-base diagnosis is the most interesting for deployed systems, it was shown to be less accurate than its capacity counterpart for the tested algorithms. Yet, the accuracy of the diagnosis is still satisfactory for days where cloud effect is limited (average RMSE of 2.5% compared to 1.3% for capacity based if 50% or more clear skies).Summarizing, we will show that the degradation of PV-connected batteries could be diagnosed opportunistically without the need for maintenance cycles using well-trained state-of-art machine learning algorithms. Calculated RMSEs were below 2% for days with 50% or more clear skies considering more than 10,000 different degradation paths with 25% or less of the three thermodynamic degradation modes. In addition to the results themselves, and before real PV battery degradation data becomes available, this work is showing the significant benefits of using synthetic data to understand the expected variations and to start preparing adapted tools for when data become available.

Comparative Study of Incremental Capacity Curve Determination Methods for Lithium-Ion Batteries Considering the Real-World Situation

The incremental capacity analysis (ICA) method is a promising method in battery state of health (SOH) estimation studies. The incremental capacity (IC) curve determination is one of the critical parts of the ICA method. However, the uncertain and incomplete charging conditions of real-world electric vehicles (EVs) significantly limit the IC curve determination. This article provides a thorough analysis of the practicality and limitations of four IC curve determination methods based on the datasets collected from real-world operating EVs with a comprehensive comparison scheme. The Lorentz function fitting method is improved by the differential evolution algorithm, breaking the limitation of fixed parameter boundary constraints. A novel PCHIP method is further proposed to determine the IC curve, with higher robustness to realistic uncertain and incomplete charging conditions. The proposed method is validated by real-world data from 40 EVs with low sampling frequency. The results show the extracted features from the IC curves, determined by the proposed method have a stronger correlation with the SOH, allowing the accurate SOH estimation with a 2% error. With less computational resources and sampling frequency requirements, this method shows great potential for the realistic battery management system implementation.

A generic lithium-ion battery integration model considering multiphysics

Battery is widely suggested as a reservoir to fasten the power balance between supply and demand. Amongst a variety of batteries, lithium-ion battery technology is the most promoted. However, most of the battery models in the power system simulation are assumed that the battery has a fixed electrical performance, while in reality, this is related to various factors, such as temperature and aging. In order to precisely model the battery, this paper proposes an easy-to-use lithium battery model considering multiphysics, including electrodynamic field, thermodynamic field, and lifetime field. The parameter identification of the proposed model has also been provided. Real battery experimental data serves to verify the accuracy of the proposed model. The simulation results prove that the temperature and aging can significantly degrade battery performance, the process of which can be well captured by the proposed model.

Heterogeneous current and concentration distributions in a redox flow battery: Influence on the cell voltage

In order to analyze the heterogeneity of the local behavior of an organic redox flow battery, a dynamic two-dimensional model was built at the engineering scale, combining mass and charges transfer, and electrochemical. It is able to simulate correctly the local behavior of a real segmented organic battery and allows to bring additional information about the effect of the internal heterogeneities on the performances. During charge-discharge cycling, the link between the local current density variation and the Faradaic source of charge and reactant depletion was established, and the effect of the concentration distribution during polarization curves was also analyzed. The influence of some parameters of the model shown some unexpected results on the current density profile observed at the negative collector plate. Analysis tools such as the standard deviations of the local current density and of the redox species concentration were developed in order to quantify their internal distribution giving quantitative criteria for redox flow battery performances. Although ignoring transport phenomena at the fiber level, the model reveals the influence of the concentration distribution on the cell behavior and mass-transport limitations at the macroscopic level.

Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses

Foreign matter defect introduced during lithium-ion battery manufacturing process is one of the main reasons for battery thermal runaway. Therefore, reliable detection of the foreign matter defect is needed for safe and long-term operation of lithium-ion batteries. It is favored to detect the defective battery during the battery manufacturing process before the battery is put into use. In this study, the defects are implanted into batteries on a real battery pilot manufacturing line. Data of defective batteries and thousands of normal batteries are collected for data analyses and algorithm development. Feature selection is conducted with feature importance analysis using the random forest method and out-of-bag error calculation. Local outlier factor method is used for defect detection with the selected features as input. The proposed defect detection algorithm achieves high detection rate and low false alarm rate which has the potential to be deployed on the manufacturing execution system to further enhance screening ability of defective batteries and improve battery safety.

Sosnowskyi Hogweed-Based Hard Carbons for Sodium-Ion Batteries

Sodium-ion battery technology rapidly develops in the post-lithium-ion landscape. Among the variety of studied anode materials, hard carbons appear to be the realistic candidates because of their electrochemical performance and relative ease of production. This class of materials can be obtained from a variety of precursors, and the most ecologically important and interesting route is the synthesis from biomass. In the present work, for the first time, hard carbons were obtained from Heracleum sosnowskyi, a highly invasive plant, which is dangerous for humans and can cause skin burns but produces a large amount of green biomass in a short time. We proposed a simple synthesis method that includes the pretreatment stage and further carbonization at 1300 °C. The effect of the pretreatment of giant hogweed on the hard carbon electrochemical properties was studied. Obtained materials demonstrate >220 mAh g−1 of the discharge capacity, high values of the initial Coulombic efficiency reaching 87% and capacity retention of 95% after 100 charge-discharge cycles in sodium half-cells. Key parameters of the materials were examined by means of different analytical, spectroscopic and microscopic techniques. The possibility of using the giant hogweed-based hard carbons in real batteries is demonstrated with full sodium-ion cells with NASICON-type Na3V2(PO4)3 cathode material.

Operando monitoring of single-particle kinetic state-of-charge heterogeneities and cracking in high-rate Li-ion anodes

To rationalize and improve the performance of newly developed high-rate battery electrode materials, it is crucial to understand the ion intercalation and degradation mechanisms occurring during realistic battery operation. Here we apply a laboratory-based operando optical scattering microscopy method to study micrometre-sized rod-like particles of the anode material Nb14W3O44 during high-rate cycling. We directly visualize elongation of the particles, which, by comparison with ensemble X-ray diffraction, allows us to determine changes in the state of charge of individual particles. A continuous change in scattering intensity with state of charge enables the observation of non-equilibrium kinetic phase separations within individual particles. Phase field modelling (informed by pulsed-field-gradient nuclear magnetic resonance and electrochemical experiments) supports the kinetic origin of this separation, which arises from the state-of-charge dependence of the Li-ion diffusion coefficient. The non-equilibrium phase separations lead to particle cracking at high rates of delithiation, particularly in longer particles, with some of the resulting fragments becoming electrically disconnected on subsequent cycling. These results demonstrate the power of optical scattering microscopy to track rapid non-equilibrium processes that would be inaccessible with established characterization techniques.

Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors

Lithium-ion battery (LIB) has been deployed for the electrification of the transport sector as a key strategy for climate change mitigation and adaptation. However, it has significant technical challenges such as thermal runaway, requiring a good understanding and accurate prediction of the LIB thermal behavior (heat generation rate). In this study, a novel hybrid approach using an Artificial Neural Network (ANN) is developed for predicting the heat generation rate with discharge current, output voltage, ambient temperature, cell surface temperature, and Depth of Discharge (DOD) as the feature vectors (inputs); where the DOD is estimated with an Extended Kalman Filter (EKF), and direct Coulomb Counting (CC) method, respectively. A shallow neural network utilizing the Marquette-Levenberg algorithm is designed and calibrated using over 8000 cases of the testing data. It is shown that the predicted heat generation rate of LIB agrees well with the experimental results with an accuracy of R > 0.995. Further potential of this hybrid data-based model is evaluated by simulating a thermal management system control and by introducing voltage and current sensor faults for diagnostic purposes. It is shown that, when compared to the experimental value, the relative error of the total heat output generated is less than 2% when there is no sensor fault, and greater than 50% and 25%, respectively, with an induced failure of the current and voltage sensor, demonstrating the ability to build accurate models relying solely on LIB discharge data for sensor diagnostics. This study highlights the combination of using battery thermal behavior with machine learning for real time battery system monitoring, controls and field diagnostics.

Manipulating Hierarchical Orientation of Wet-Spun Hybrid Fibers via Rheological Engineering for Zn-Ion Fiber Batteries.

Wet-spinning is a promising strategy to fabricate fiber electrodes for real commercial fiber battery applications, according to its great compatibility with large-scale fiber production. However, engineering the rheological properties of the electrochemical active materials to accommodate the viscoelasticity or liquid crystalline requirements for continuous wet-spinning remains a daunting challenge. Here, with entropy-driven volume-exclusion effects, the rheological behavior of vanadium pentoxide (V2 O5 ) nanowire dispersions is regulated through introducing 2D graphene oxide (GO) flakes in an optimal ratio. By optimizing the viscoelasticity and liquid-crystalline behavior of the spinning dope, the wet-spun hybrid fibers display controlled hierarchical orientation. The wet-spun V2 O5 /rGO hybrid fiber with the optimal 10:1 mass fraction (V2 O5 /rGO10:1 ) exhibits a highly oriented nanoblock arrangement, enabling efficient Zn-ion migration and an excellent Zn-ion storage capacity of 486.03 mAh g-1 at 0.1 A g-1 . A half-meter long quasi-solid-state fiber Zn-ion battery is assembled with a polyacrylamide gel electrolyte and biocompatible Ecoflex encapsulation. The thus-derived fiber Zn-ion battery is integrated into a wearable self-powered system, incorporating a highly efficient GaAs solar cell, which delivers a record-high overall efficiency (9.80%) for flexible solar charging systems.

Identification of Overpotentials in Vanadium Redox Flow Battery with Reference Electrodes and Determination of Apparent Electrochemical Rate Constants

Vanadium redox flow battery (VRFB) is an amateur technology that passed pilot stages in fulfilling the stationary applications at the energy storage market with a high share of renewable power generation. The commercial applications of VRFBs require the optimization of their internal losses and developing the optimal control strategies for in-grid operation. This calls for a deep understanding of the internal VRFB processes, which greatly benefit from both the mathematical modeling and experimental testing for the model validation and refinement.However, the available experimental data for VRFBs is usually limited by open-circuit voltage (OCV) and potential (OCP), charge/discharge voltage profiles at constant load current, full cell polarization curves at the fixed state of charge (SOC), and internal stack ohmic resistances. This also complicates the decoupling and accessing the voltage loss sources, with the prevailing ones usually classified as ohmic, concentration, and activation overpotentials. As such, Aaron et al. (1) showed that decoupling of overpotentials at positive and negative half-cell requires the half-cell potentials measurements with reference electrodes (RE) in addition to full cell data. Different configurations of RE incorporation (2–4) were suggested with a limited agreement with full cell experimental data. The alternative approach with extrapolation of parameters presented in the literature is also challenging due to their strong dependence on an experimental protocol (e.g., the electrode material and its pre-treatment procedure, a membrane, and a setup geometry). As an illustration, the comprehensive review Roznyatsovskaya et al. (5) highlights the four orders of magnitude difference in reported surface area-normalized rate constants. Therefore, the development of a self-consistent experimental setup is crucial for understanding and decoupling internal losses for flow batteries. Combined with VRFB modeling, this may extend accurate predictions of its operation at extreme SOCs and load currents, where voltage losses hamper the efficiency of the battery with the dominating ones usually attributed to concentration (mass-transport in solutions) and activation (sluggish kinetics of reactions on electrodes) overpotentials.Herein, we developed the experimental setup adopting a 5cm2 single flow cell equipped with reference electrodes at positive and negative sides. The experiments were performed in symmetric and full cell designs. The polarization curves were measured at different SOCs [10 – 90 %], electrolyte flow rates [5-30 ml min-1], and load currents. The additional effort was made to compare the results of the experiments in symmetric and full cell designs and extract the apparent kinetic rate constants for positive and negative half-cells. The latter was performed within the 0D modeling approach, which is based on general mass and charge conservation principles. The key feature of the model is the approach for the determination of the integral mass-transfer coefficient on the electrode-electrolyte interface that allows describing non-linear polarization behaviour at high current densities. The effective thermodynamically consistent single-step reaction mechanisms were considered at both half cells, thus decreasing the number of free kinetic parameters. Overall, the proposed approach demonstrated the growing impact of the membrane losses at high current densities. The extracted apparent rate constants under conditions of a real flow battery provide important insights for developing reliable and efficient VRFBs applicable for energy storage systems.1 D. Aaron, Z. Tang, A. B. Papandrew and T. A. Zawodzinski, J. Appl. Electrochem., 2011, 41, 1175–1182.2 H. Lim, J. S. Yi and D. Lee, J. Power Sources, 2019, 422, 65–72.3 S. Ressel, A. Laube, S. Fischer, A. Chica, T. Flower and T. Struckmann, J. Power Sources, 2017, 355, 199–205.4 M. Cecchetti, A. Casalegno and M. Zago, J. Power Sources, 2018, 400, 218–224.5 N. Roznyatovskaya, J. Noack, K. Pinkwart and J. Tübke, Curr. Opin. Electrochem., 2020, 19, 42–48.

(Keynote) Pro and Con of Bi-Decorated Carbon Felts in Vanadium Redox Flow Batteries

Carbon felts are commonly utilized electrodes in vanadium flow batteries due to their low cost, light weight, good electronic conductivity, and chemical inertness. In contrast, their activity in catalyzing the vanadium redox reactions is less remarkable. In particular at the negative side, where V3+ reduction competes with the parasitic hydrogen evolution reaction (HER) at low potentials. In the literature, the addition of Bi3+ into the electrolyte [1] or its deposition on the felts has been suggested as a remedy. However, it is not clear yet, whether the Bi decoration remains stable with time and cycling and which mechanisms are responsible for the observed performance increase.The aim of this work is therefore to unravel more details about the catalyzing effect of Bi and its stability on the cell level. To this effect, the electrode kinetics with and without Bi decoration were studied comparing results obtained using Friedl’s method [2] with cyclic voltammetry (CV) data analyzed by the Polarographica curve fitting routine [3]. This approach can also be generalized to obtain precise kinetic information with other decorating metals.We furthermore applied negative dynamic hydrogen bubble templating (DHBT) deposition as an interesting way to coat commercially available carbon felts with a structured metallic layer of high surface area. Using this strategy, a significant positive effect was observed. However, the drying parameters and post-processing of the felts are important parameters which severely affect the final performance.In order to study the stability of Bi deposits in realistic battery experiments, in-situ synchrotron radiography has been used to monitor the Bi-decorated felts during charge-discharge cycles [4]. The figure demonstrates the effect of different flow rates on the Bi deposition patterns. Dark spots show metal-decorated areas within the felts at flow rates of 5 mlmin-1 (left), 15 mlmin-1 (middle) and 25 mlmin-1 (right). It can be easily seen that the operation conditions influence the felt decoration significantly, while Bi deposits dissolve and deposit during the charge-discharge routine. Further studies will be necessary to find out a) whether different deposit positions affect the performance enhancement and b) if DHBT-Bi remains more stable under operating conditions.In our presentation, we will not only focus on the materials, but also highlight techniques especially suitable for the stuctural and electrochemical analysis of felt electrodes.[1] B. Li, M. Gu, Z. Nie, Y. Shao, Q. Luo, X. Wei, X. Li, J. Xiao, C. Wang, V. Sprenkle, W. Wang, Bismuth Nanoparticle Decorating Graphite Felt as a High-Performance Electrode for an All-Vanadium Redox Flow Battery, Nano Letters 13(3) (2013) 1330-1335.[2] J. Friedl and U. Stimming, Determining Electron Transfer Kinetics at Porous Electrodes, Electrochim. Acta 227 (2017) 235–245.[3] T. Tichter, J. Schneider, D. Andrae, M. Gebhard, and C. Roth, Universal algorithm for simulating and evaluating cyclic voltammetry atmacroporous electrodes by considering random arrays of microelectrodes, ChemPhysChem 21 (2019) 428 –441.[4] M. Gebhard, T. Tichter, J. Schneider, J. Mayer, A. Hilger, M. Osenberg, M. Rahn, I. Manke, C. Roth J. Power Sources 478 (2020) 228695. Figure 1