Batteries & Supercaps最新文献

In this work, Density Functional Theory (DFT) is used to study pristine and defective GDY. We investigate the effect of Li atom adsorption on the electronic and structural properties of this 2D material. In both cases, the Li atom is located at the corner of the triangular-like pore (H1), but with a slight shift for the defective system. In the perfect system, the Li−C bond distances range from 2.289 Å to 2.461 Å, while in the defective case, they range from 2.237 Å to 3.184 Å. In the perfect case, the GDY−Li system becomes metallic and the Li 2 s states are stabilized. Charge transfer to the surfaces occurs near the vicinity of the Li atom. The C vacancy generates new C=C bonds similar to double bonds, enhancing the interaction with Li through strong conjugation. After Li adsorption, the sum of bond order for all the C atoms increases in both structures, from 0.4 % to 6 %. The Li storage capacity without significant restructuring is six Li atoms. When the atom concentration increases, the OCV values for Li decrease from 0.93 V to 0.23 V. For defective GDY, the specific capacity is 788 mAhg₋₁, which is slightly higher than for pristine case.

The metal-ion battery manufacturing growth rates increase attention to the safety issues. For promising sodium-ion batteries, this topic has been studied in much less detail than for the lithium-ion ones. Here, we explored the thermal runaway process of Na-ion pouch cells with the Na₃V₂O₂(PO₄)₂F (NVOPF)-based cathode. The thermal runaway onset temperature for such cells is noticeably higher than that for the NMC-based LIBs. We show that thermal runaway is triggered by the anode and the separator decomposition rather than by the processes at the cathode. The composition of the gas mixture released during thermal runaway process is similar to that for Li-ion batteries. The results suggest that sodium-ion batteries based on polyanionic cathodes can pave the way to safer metal-ion energy storage technologies.

Long-duration energy storage (LDES) technologies are pivotal for the adoption of renewables like wind and solar. Non-aqueous redox flow batteries (NARFBs) with a sodium-polysulfide hybrid system feature high energy density independent of power density, yet face challenges with polysulfide shuttling. This study investigates a hydrocarbon-based penta-block copolymer membrane, Nexar, to mitigate crossover effects by balancing TFSI conversion and their crosslink density. The membranes are annealed to induce crosslinking for reducing electrolyte uptake and enhancing mechanical stability while demonstrating excellent ionic conductivity. The hydrocarbon-based membranes address environmental concerns associated with perfluoroalkyl substances and improve the performance and durability of NARFBs. Our findings suggest that annealed Nexar membranes with tailored TFSI functionality offer a scalable, cost-effective solution for enhancing the efficiency of high-capacity energy storage systems, pivotal for grid integration of renewable sources.

Aqueous zinc-iodine (Zn-I₂) batteries featuring abundant raw materials, inherent safety, excellent cost competitiveness and environmental benignity have been identified as one kind of important electrochemical energy storage devices. However, these batteries always suffer from inferior electrochemical performance, because of dendrite growth and corrosion/passivation of the anodes. Herein, a copper iodide-polyvinylpyrrolidone (CuI-PVP) composite layer is proposed to suppress the parasitic reactions and protect the Zn anodes. In this layer, the CuI can spontaneously react with metallic Zn and convert into Cu and Cu₅Zn₈ (2CuI+Zn→2Cu+ZnI₂; 5Cu+8Zn→Cu₅Zn₈). The highly zincophilic Cu and Cu₅Zn₈, as heterogeneous seeds, can guide the uniform Zn nucleation and deposition, while alleviating corrosion of the Zn anodes. At the same time, the iodide species releasing from the composite layer can be oxidized and deposited on the cathodes, contributing additional capacity. As a result, the symmetric cell prepared with the CuI-PVP@Zn anodes demonstrates a long cycling lifetime of 1400 hours at 1 mA cm⁻² and 1 mAh cm⁻². Under an even higher current density of 5 mA cm⁻², the CuI-PVP@Zn cell can still stably work for more than 660 hours. The practical application of this CuI-PVP@Zn electrode has been further demonstrated in Zn-I₂ full batteries, which achieve 60 % higher specific capacity than the untreated ones (251.4 vs. 157.1 mAh g⁻¹ after 2800 cycles).

The study of flow batteries (FBs) requires the development of tools able to evaluate their performance during operation in a reliable and simple way. In this work, we present an experimental set-up that allows the on-line monitoring of the half-cells state of charge and apparent overpotentials on the positive and negative electrodes during battery operation. These measurements are feasible by using additional flow cells that include a reference electrode on each side. We used the experimental set-up to study the performance of the vanadium system as well as a previously reported stable organic couple. The studies consisted on short cycling operation at different current densities and polarization curves at different flow rates and states of charge. By confirming previous results obtained for vanadium-FBs and extending the analysis to further systems, we demonstrated that this approach provides a reliable deeper insight into the battery performance and the processes taking place during operation.

The Cover Feature showcases the diverse applications of Prussian Blue analogue (PBA)–based post-lithium batteries (PLBs). The circles on the left of the battery depict their current use in supporting the transition to clean energy. The circles on the right highlight potential future industries that PBA-based PLBs could transform, including aerospace, electronics, and mobility applications. The development of PBA cathodes is poised to be a significant breakthrough in enhancing PLBs, unlocking a wide array of applications. More information can be found in the Review by J. D. Ocon and co-workers (DOI: 10.1002/batt.202400280).

In this work, we have designed an all-organic and all-solid-state lithium metal battery based on 7,7,8,8-tetracyano-p-quinodimethane (TCNQ) as the organic electroactive material and a COF (Covalent Organic Framework)/PEO (PolyEthylene Oxide) composite as solid electrolyte. The use of a solid electrolyte allows fixing the solubility problem of organic electroactive materials in classical liquid electrolytes. This is the first time an all-solid-state organic battery based on TCNQ versus lithium metal is reported, since no liquid additive was included in the formulation of the electrolyte. We obtained a reversible capacity of 88 mAh g⁻¹ at the second discharge, and still 58 mAh g⁻¹ at the tenth discharge. The redox processes were investigated by X-ray Photoelectron Spectroscopy (XPS). We could evidence the involvement of the two lithiation steps of TCNQ (LiTCNQ and Li₂TCNQ) in the reversible capacity. Optimization of the electrode manufacturing and formulation, and replacing the salt (LiI) by alternative ones opens the door to future improvements in the electrochemical performances. This study demonstrates the interest of COF-type organic structures in the formulation of organic solid electrolytes.

The Front Cover illustrates an ethanol solution phase–synthesized sulfide solid electrolyte with a characteristic core–shell structure; it produces a suitable functionalized interface at the sulfide solid electrolyte/cathode active material interface for all-solid-state batteries (ASSBs). This study is expected to provide fundamental and industrial insights for the practical implementation of ASSBs. More information can be found in the Research Article by Y. Morino and co-workers (DOI: 10.1002/batt.202400264).

The Cover Feature illustrates lithium-ion battery degradation. It demonstrates how individual aging modes—the loss of accessible active material from an electrode or the depletion of cyclable lithium ions—affect the capacities and balancing between the electrodes. These changes are visualized by color-coded surfaces that represent electrode potentials in the full cell′s cyclation window, transitioning from green to red to indicate degradation. Such alterations lead to a measurable capacity fade and changes in the full cell′s potential curve, as depicted by the differential voltage curve. The underlying work combines this mechanistic model with a data-driven model approach of the individual aging modes to predict both capacity fade and changes to the potential curve under various aging conditions. This will help to enhance understanding and prediction of battery degradation and can be the basis for a more precise onboard state-of-charge and state-of-health estimation of degraded batteries. More information can be found in the Research Article by J. Stadler and co-workers (DOI: 10.1002/batt.202400211).

The accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium-ion batteries. Data-driven SOC estimation is considered an emerging and effective solution. However, existing data-driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short-term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional-integral-derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.