The flow field design and material composition of the electrode plays an important role in the performance of redox flow batteries, especially when using highly viscous liquids. To enhance the discharge power density of zinc slurry air flow batteries, an optimum slurry distribution in the cell is key. Hence, several types of flow fields (serpentine, parallel, plastic flow frames) were tested in this study to improve the discharge power density of the battery. The serpentine flow field delivered a power density of 55 mW∙cm⁻², while parallel and flow frame resulted in 30 mW∙cm⁻² and 10 mW∙cm⁻², respectively. Moreover, when the anode bipolar plate material was changed from graphite to copper, the power density of the flow frame increased to 65 mW∙cm⁻², and further improvement was attained when the bipolar plate material was further changed to copper–nickel. These results show the potential to increase the power density of slurry-based flow batteries by flow field optimization and design of bipolar plate materials.

The performance of redox flow batteries is notably influenced by the electrolyte, especially in slurry-based flow batteries, as it serves as both an ionic conductive electrolyte and a flowing electrode. In this study, carbon additives were introduced to achieve a rechargeable zinc slurry flow battery by minimizing the zinc plating on the bipolar plate that occurs during charging. When no carbon additive was present in the zinc slurry, the discharge current density was 24 mA∙cm⁻² at 0.6 V, while the use of carbon additives increased it to up to 38 mA∙cm⁻². The maximum power density was also increased from 16 mW∙cm⁻² to 23 mW∙cm⁻². Moreover, the amount of zinc plated on the bipolar plate during charging decreased with increasing carbon content in the slurry. Rheological investigation revealed that the elastic modulus and yield stress are directly proportional to the carbon content in the slurry, which is beneficial for redox flow battery applications, but comes at the expense of an increase in viscosity (two-fold increase at 100 s⁻¹). These results show how the use of conductive additives can enhance the energy density of slurry-based flow batteries.

Li-ion batteries are garnering significant attention due to the electrification of critical sectors. High-fidelity battery cell models have proven effective in assessing performance and optimizing design, alleviating the financial burden associated with extensive experimental procedures. However, the computational costs associated with such simulations can become prohibitive, particularly when numerous iterations are required or when integration into small devices, such as battery management systems, is necessary. To address these challenges and provide an alternative to traditional methods such as finite element and finite volume solvers, we propose the development of an algorithm that utilizes Physics-Informed Neural Networks (PINNs) to solve the Single Particle Model across multiple parameter ranges. A notable advantage of this machine learning approach is its capacity to generate competitive solutions post-training by relying solely on the governing equations, without the necessity for experimental or simulation data. Additionally, the lightweight nature of the model indicates its potential for embedding within small-scale devices.

Li‐ion batteries are attracting an increasing attention due to the process of electrification involving different industrial sectors. Many efforts are dedicated to improving battery performance in terms of cyclability, capacity, fast charging and safety to name a few. Therefore, it is of primal importance to identify and understand the degradation modes that stay behind cell failure. In this sense, Loss of Lithium Inventory (LLI) and Loss of Active Material (LAM) are considered fundamental indicators for estimating the state of health of a cell. In this work, we introduce an automated open tool which is able to return LLI, LAM and voltage drop due to and eventual development of an internal resistance by fitting experimental pseudo‐OCV profiles of a cell, at its beginning and end of life, with a simulated curve obtained from the pseudo‐OCP curves of positive and negative electrodes. The tool is based on a mathematical model consisting in linear transformations that keeps it simple and guarantees for low computational cost. The model is validated against bibliographic dataset and tested on experimental curves from lab‐scale coin and commercial cylindrical cell with different chemistries.

Anode‐free batteries (AFBs) are one of the most discussed battery concepts due to their potential advantages in terms of energy density, reduced manufacturing costs, and improved sustainability compared to conventional lithium‐ion and lithium metal batteries. However, many fundamental and practical challenges remain to be overcome in order to realize their full potential. This Perspective provides a critical overview of the latest advances in liquid and solid electrolyte AFBs, including an analysis of practical cell performance and an assessment of the advantages and challenges of this cell concept. Since the processes at the negative current collector are central to the electrochemical performance of AFBs, they are discussed in detail, with a focus on metal plating/stripping mechanisms and key degradation processes, as well as strategies for optimizing electrode properties to enable stable cycling. In addition, the associated processes in other cell components (positive electrode, electrolyte, and critical interfaces) are discussed to understand their influence on the overall performance of the cell. Finally, we identify critical gaps in understanding, data accessibility, reporting standards, and metrics that need to be addressed to guide future research and the transition from laboratory scale to practical, high‐performance devices.