Accurate estimation of state of charge (SoC) and maintaining balanced charge levels across secondary battery cells are crucial in battery management systems (BMSs) to extend battery life while improving the performance and thermal stability of Li-ion batteries (LIBs) in electric vehicles (EVs). However, there are still underexplored challenges associated with circulating currents in electrochemical cells during continuous operation which can overheat battery packs, reducing their life span or result in dangerous thermal runaways. This paper investigates SoC estimation using various real-world charging and discharging profiles, along with charge-balancing strategies to enhance the longevity of parallel-connected Li-ion battery cells. A newly developed hedge feedforward feedback-based gated recurrent unit with H∞ controller (HFF-GRU-H∞) is introduced to improve the SoC estimation accuracy with comparisons to nine widely-applied deep-learning algorithms. Moreover, SoC balancing for three individual battery cells is achieved using a bidirectional DC/DC power converter controlled by an H∞ robust control system during charging-discharging cycles. The experimental results indicate that SoC capacity estimation error can be reduced to 0.043%. Also, the applied optimization algorithm minimized the determination time to 0.477 s when benchmarked with existing methods leading to better charge balance among the battery cells. As a result, the overall battery pack lifespan can be extended by 27.7%, offering substantial advantages for industrial applications.

As the global demand for energy storage grows, sodium-ion batteries (SIBs) are emerging as a cost-effective and resource-abundant alternative to lithium-ion batteries (LIBs). However, despite their increasing deployment, efficient end-of-life recycling strategies for SIBs remain underdeveloped. In this work, a novel two-step hydrometallurgical process is presented for recovering critical metals from spent SIB cathodes. The first step employs mild citric acid leaching, achieving recovery efficiencies above 95% for nickel (Ni), manganese (Mn), sodium (Na), and iron (Fe). This performance not only demonstrates the effectiveness of citric acid as a biodegradable leaching agent but also fulfils forthcoming European Union requirements, which mandate 90% Ni recovery by 2027 and 95% by 2031. In the second step, selective precipitation with oxalic acid enables targeted recovery of Ni and Mn, with precipitation yields exceeding 95%. The resulting Mn-Ni mixed oxalate is a promising precursor for resynthesizing Na-Ni-Fe-Mn cathodes or other advanced applications. Overall, this integrated method introduces an environmental and closed-loop recycling route that advances circular economy principles and supports the sustainable transition from LIBs to next-generation SIB technologies.

Nitrogen-doped graphene aerogels (NGAs) have attracted much attention as next-generation electrode materials for supercapacitors because of their high surface area, excellent conductivity, and chemical tunability. Recent studies have confirmed how nitrogen doping can improve pseudocapacitive behaviour, wettability, and electron transport, thus significantly improving the specific capacitance, energy density, and cycling performance. This review analyses the different synthesis strategies, such as hydrothermal self-assembly, sol-gel polymerisation, and template-directed synthesis, and shows the electrochemical performance obtained from both symmetric and asymmetric set-ups. The best-performing NGAs have demonstrated specific capacitances reaching 900 F/g, energy densities of over 60 Wh/kg, and long-term retention exceeding 90% over 10,000 cycles. Nonetheless, multiple synthesis strategies are still limited by batch processing, excessive thermal demand, and difficulty with dopant homogeneity. Details on the electrode configuration and performance reported between studies are inconsistent, making direct comparisons challenging and hindering industrial translation. This review highlights the critical demand for scalable, greener synthesis protocols, standardised testing protocols, and systematic evaluations of the role of nitrogen species in capacitance enhancement. This work can be extended to dual-doping, flexible electrode fabrication, and the incorporation of the doped material into practical device architectures. Such insights provide a basis for rationally designing high-performance N-GAs for supercapacitors.

Active battery balancing is essential for maximizing the performance and safety of lithium-ion battery packs in electric vehicles and energy storage systems, yet traditional control methods struggle with nonlinear dynamics. This paper investigates the critical role of state-space design in tabular Q-learning for controlling switches of a buck-boost converter in a four-cell pack, addressing a key gap in the application of reinforcement learning to battery management systems. We propose and compare three novel discrete state representations: a coarse 11-state pairwise comparison, an intermediate 27-state hierarchical relational model, and a fine-grained 81-state individual deviation model. Through simulations across 1000 training episodes and 24 test scenarios, the 27-state model achieves superior convergence, with an average balancing time of around 41 timesteps and the lowest performance variance (σ = 12.28). Statistical analysis and state-transition graphs reveal that this optimal granularity enables hierarchical control strategies, balancing informational richness with learnability to avoid perceptual aliasing and the curse of dimensionality. These findings provide a blueprint for designing efficient RL policies in BMS, which has implications for scalable and real-time implementations in high-voltage applications.

The transition from liquid to solid electrolytes is driven by the need for enhanced safety and higher energy density in advanced batteries. Solid-state electrolytes (SSEs) eliminate flammability and leakage risks but suffer from low ionic conductivity at ambient conditions due to lattice constraints and high migration barriers. Breakthroughs in SSEs materials such as Li₁₀GeP₂S₁₂ (LGPS), Li₇La₃Zr₂O₁₂ (LLZO), and Argyrodite-type Li₆PS₅Cl reveal a unique phenomenon: lithium ions exhibit “floating” behavior within a stable anionic framework, enabling quasi-fluid migration through interconnected channels. This work explores the physicochemical nature of “floating Li,” emphasizing weak interactions, multi-path coupling, and framework flexibility as key factors reducing migration barriers. We further propose an electronic-density-based approach using the interaction region indicator (IRI) to extract characteristic descriptors for high-conductivity SSEs. Comparative analysis of IRI maps across different electrolytes demonstrates distinct patterns associated with low-electron-density migration channels. These insights establish a paradigm shift from single-path models to networked migration behavior and suggest that integrating chemical bonding theory, lattice dynamics, and data-driven screening can accelerate the rational design of next-generation solid electrolytes.

Two-dimensional (2D) MXenes, a family of transition metal (TM) carbides and nitrides, have rapidly reshaped the landscape of electrochemical energy storage owing to their rich chemistry and outstanding charge-storage performance. High-entropy MXenes (HE-MX), which integrate five or more near-equimolar TMs within a single two-dimensional (2D) lattice, extend this platform by introducing entropy-stabilised multielement configurations inspired by high-entropy alloys (HEAs). In these materials, configurational entropy, lattice distortion and “cocktail” effects cooperatively enhance electrochemical stability, activity and durability. This review explores the development of HE-MX, tracing their evolution from the foundational concepts of HEA to sophisticated multi-component architectures with adjustable structures and functional properties. It emphasises advancements in synthesis, such as the selective etching of complex precursors and in the management of lattice strain and surface terminations. By combining insights from in situ spectroscopy, multiscale simulations and electrochemical measurements, we clarify how features such as cation ordering, tailored surface terminations and entropy-stabilised phases govern capacitive behaviour, ion transport kinetics and cycling robustness. Building on these entropy–structure–property relationships, this review outlines the design principles for atomic-level control of the composition and interfaces and identifies strategies to improve stability under extreme operating conditions and enable scalable manufacturing. HE-MX thus emerges as a versatile platform to alleviate the longstanding trade-off between energy density and durability in next-generation electrochemical energy storage systems.

Silver nanoparticles (Ag NPs) were homogeneously deposited on the surface of silicon dioxide (SiO₂) and then encapsulated by an outer titanium oxide (TiO₂) layer. This SiO₂/Ag/TiO₂ geometry (denoted as SiO₂-Ag@TiO₂ nanoreactor, where “@” denotes a gap) composite was successfully developed via a conventional sacrificial method followed by partial etching. This special SiO₂, Ag, TiO₂ bearing-construction (BC) catalyst exhibits superior catalytic and exceptional stability performance when used in the degradation of methylene blue (MB) under ultraviolet light (UV light) and visible light, compared with pure TiO₂ shell and traditional Ag/TiO₂ yolk–shell (Ag-TiO₂). This enhanced catalytic efficiency is primarily attributed to synergistic effects derived from Ag NPs “locking and guarding” mechanism in the presence of amino-SiO₂ and outer TiO₂. In this regard, our rational BC design concept proposed a state-of-the-art strategy and provided an opportunity to shorten the distance between theory and practical applications in solar conversion, such as water splitting technology, photovoltaic, and solar cells.

This work investigates the volatile fraction released from black mass (BM) obtained from spent lithium-ion batteries subjected to microwave (MW) thermal treatment. MW processing is emerging as an alternative to conventional pyrometallurgy for improving energy efficiency and recovery of critical metals such as lithium, yet the associated emission profile remains poorly characterized. However, the studies of the emissions associated with these treatments are quite limited. Here, a multilevel full factorial Design of Experiments is applied for the first time to evaluate the influence of MW power, exposure time, and BM mass on heating dynamics and lithium extraction efficiency. Volatile organic compounds generated during MW processing are identified by headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME/GC-MS), showing a complex mixture of aliphatic and aromatic hydrocarbons, carbonate esters, and phosphorus- and fluorine-containing species. Multinuclear NMR spectroscopy (¹H, ⁷Li, ¹⁹F, ³¹P) confirms the presence of electrolyte-derived residues such as Li⁺, PF₆⁻, and phosphate esters. The combined analytical approach clarifies degradation pathways during MW heating and highlights the need to monitor and mitigate the formation of potentially hazardous volatile species in future MW-assisted recycling processes. Statistical models reveal that the time to reach 600°C and the maximum temperature depend primarily on power and exposure time, while Li recovery is governed by BM mass and its interaction with power.

Accurate state-of-charge (SoC) estimation in lithium-ion batteries is crucial for efficient energy management, safe operation, and extended battery lifespan. Although sliding mode observers (SMOs) are widely used for this purpose, conventional first-order designs often suffer from chattering and slow convergence, resulting in noisy and less reliable estimation signals. This paper proposes a finite-time second-order sliding mode observer (SO-SMO) for accurate SoC estimation based on an equivalent circuit model of the battery. The proposed observer analytically derives a closed-form expression for the finite convergence time, enabling predictable estimation dynamics. Moreover, it eliminates chattering and significantly improves estimation smoothness and robustness against modeling uncertainties and measurement noise. A comparative analysis with the Extended Kalman Filter and traditional SMO demonstrates that the proposed method achieves higher estimation accuracy and faster convergence while maintaining lower computational complexity, making it well-suited for real-time applications. Theoretical analysis and simulation results confirm that the SO-SMO offers a superior balance between accuracy, robustness, and efficiency, establishing its potential for next-generation battery management systems in electric and hybrid vehicles.