Search Results (900)

Zinc (Zn)-based batteries have attracted significant interest for applications ranging from electric bikes to grid storage because of its advantageous properties like high abundance, non-toxicity and low-cost. Zn offers a high theoretical capacity of two electrons per atom, resulting in 820 mAh/g, making it a promising anode material for the development of highly energy dense batteries. However, the advancement of Zn-based battery systems is hindered by the limited availability of cathode materials that simultaneously offer high theoretical capacity, long-term cycling stability, and affordability. In this work, we present a new mixed material cathode system, comprising of a mixture of manganese dioxide (MnO₂) and copper oxide (Cu₂O) as active materials, that delivers a high theoretical capacity of ~280 mAh/g (MnO₂ + Cu₂O active material) (based on the combined mass of MnO₂ and Cu₂O) and supports stable cycling for >200 cycles at 1C. We further demonstrate the scalability of this novel cathode system by increasing the electrode size and capacity, highlighting its potential for practical and commercial applications. Full article

This paper presents a comprehensive review of research on sulfurized polyacrylonitrile (SPAN) for rechargeable batteries which was firstly reported by Jiulin Wang in July 2002. Spanning over two decades (2002–2025), this review cites over 600 publications, covering various aspects of SPAN-based battery systems. These include SPAN chemical structure, structural evolution during synthesis, redox reaction mechanism, synthetic conditions, cathode, electrolyte, binder, current collector, separator, anode, SPAN as additive, SPAN as anode, and high-energy SPAN cathodes. As this field continues to advance rapidly and garners significant interest, this review aims to provide researchers with a thorough and in-depth overview of the progress made over the past 23 years. Additionally, it highlights emerging trends and outlines future directions for SPAN research and its practical applications in energy storage technologies. Full article

Cement-based batteries (CBBs) are an emerging category of multifunctional materials that combine structural load-bearing capacity with integrated electrochemical energy storage, enabling the development of self-powered infrastructure. Although previous reviews have explored selected aspects of CBB technology, a comprehensive synthesis encompassing system architectures, material strategies, and performance metrics remains insufficient. In this review, CBB systems are categorized into two representative configurations: probe-type galvanic cells and layered monolithic structures. Their structural characteristics and electrochemical behaviors are critically compared. Strategies to enhance performance include improving ionic conductivity through alkaline pore solutions, facilitating electron transport using carbon-based conductive networks, and incorporating redox-active materials such as zinc–manganese dioxide and nickel–iron couples. Early CBB prototypes demonstrated limited energy densities due to high internal resistance and inefficient utilization of active components. Recent advancements in electrode architecture, including nickel-coated carbon fiber meshes and three-dimensional nickel foam scaffolds, have achieved stable rechargeability across multiple cycles with energy densities surpassing 11 Wh/m². These findings demonstrate the practical potential of CBBs for both energy storage and additional functionalities, such as strain sensing enabled by conductive cement matrices. This review establishes a critical basis for future development of CBBs as multifunctional structural components in infrastructure applications. Full article

The improvement of environmental conditions has become a priority for governments and legislators. New electrified mobility systems are increasingly present in our environment, as they enable the reduction of polluting emissions. Electric vehicles (EVs) are one of the fastest-growing alternatives to date, with exponential growth expected over the next few years. In this article, the various charging modes for EVs are explored, and the risks associated with charging technologies are analysed, particularly for charging systems in high-power DC with Lithium battery energy storage, given their long market deployment and characteristic behaviour. In particular, the Arc Flash (AF) risk present in high-power DC chargers will be studied, involving numerous simulations of the charging process. Subsequently, the Incident Energy (IE) analysis is carried out at different specific points of a commercial high-power ‘Mode 4’ charger. For this purpose, different analysis methods of recognised prestige, such as Doan, Paukert, or Stokes and Oppenlander, are applied, using the latest version of the ETAP^® simulation tool version 22.5.0. This study focuses on quantifying the potential severity (consequences) of an AF event, assuming its occurrence, rather than performing a probabilistic risk assessment according to standard methodologies. The primary objective of this research is to comprehensively quantify the potential consequences for workers involved in the operation, maintenance, repair, and execution of tasks related to EV charging systems. This analysis makes it possible to provide safe working conditions and to choose the appropriate and necessary personal protective equipment (PPE) for each type of operation. It is essential to develop this novel process to quantify the consequences of AF and to protect the end users of EV charging systems. Full article

Traffic crashes and congestion generate high social and economic costs, yet traditional traffic monitoring methods, such as police patrols, fixed cameras, and helicopters, are costly, labor-intensive, and limited in spatial coverage. This paper presents a novel Drone Routing and Scheduling with Flexible Multiple Visits (DRSFMV) framework, an optimization model for planning drone-based highway monitoring under realistic operational constraints, including battery limits, variable monitoring durations, recharging at a depot, and target-specific inter-visit time limits. A mixed-integer nonlinear programming (MINLP) model and a linearized version (MILP) are presented to solve the problem. Due to the NP-hard nature of the underlying problem structure, a heuristic solver, Hexaly, is also used. A case study using real traffic census data from three Southern California counties tests the models across various network sizes and configurations. The MILP solves small and medium instances efficiently, and Hexaly produces high-quality solutions for large-scale networks. Results show clear trade-offs between drone availability and time-slot flexibility, and demonstrate that stricter revisit constraints raise operational cost. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

In applications where it is crucial that a battery is recharged from the partially discharged state in the minimum time, it is crucial to honor the technological constraints related to maximum safe battery terminal voltage and maximum continuous charging current prescribed by the battery cell manufacturer. To this end, this contribution outlines the design and comprehensive simulation analysis of an adaptive battery charging system relying on battery open-circuit voltage estimation in real time. A pseudo-random binary sequence test signal and model reference adaptive system are used for the estimation of lithium titanate battery cell electrical circuit model parameters, with the design methodology based on the Lyapunov stability criterion. The proposed adaptive charger is assessed against the conventional constant-current/constant-voltage charging system. The effectiveness of the real-time parameter estimator, along with both the adaptive and traditional charging systems for the lithium titanate battery cell, is validated through simulations and experiments on a dedicated battery test bench. Full article

The scope of this paper is the development of a cost-effective wireless torque measurement system for vehicle drivetrain shafts. The prototype integrates strain gauges, an HX711 conditioner, a Wemos D1 Mini ESP8266, and a rechargeable battery directly on the rotating shaft, forming a self-contained sensor node. Calibration against a certified dynamometric wrench confirmed an operating span of ±5–50 N·m. Within this range, the device achieved a mean absolute error of 0.559 N·m. It also maintained precision better than ±2.5 N·m at 95% confidence, while real-time data were transmitted via Wi-Fi. The total component cost is below EUR 30 based on current prices. The novelty of this proof-of-concept implementation demonstrates that reliable, IoT-enabled torque sensing can be realized with low-cost, readily available parts. The paper details assembly, calibration, and deployment procedures, providing a transparent pathway for replication. By aligning with Industry 4.0 requirements for smart, connected equipment, the proposed torque measurement system offers an affordable solution for process monitoring and predictive maintenance in automotive and industrial settings. Full article

MXenes, a family of two-dimensional carbide and nitride nanomaterials, have demonstrated significant promise across various technological domains, particularly in energy storage applications. This review critically examines scalable synthesis techniques for MXenes and their potential integration into next-generation rechargeable battery systems. We highlight both top-down and emerging bottom-up approaches, exploring their respective efficiencies, environmental impacts, and industrial feasibility. The paper further discusses the electrochemical behavior of MXenes in lithium-ion, sodium-ion, and aluminum-ion batteries, as well as their multifunctional roles in solid-state batteries—including as electrodes, additives, and solid electrolytes. Special emphasis is placed on surface functionalization, interlayer engineering, and ion transport properties. We also compare MXenes with conventional graphite anodes, analyzing their gravimetric and volumetric performance potential. Finally, challenges such as diffusion kinetics, power density limitations, and scalability are addressed, providing a comprehensive outlook on the future of MXenes in sustainable energy storage technologies. Full article

Rechargeable aqueous zinc-ion batteries (AZIBs) have garnered significant research attention in the energy storage field owing to their inherent safety, cost-effectiveness, and environmental sustainability. Nevertheless, critical challenges associated with zinc anodes—including dendrite formation, hydrogen evolution corrosion, and mechanical degradation—substantially impede their practical implementation. Grain boundary engineering (GBE) emerges as an innovative solution for zinc anode optimization through the precise regulation of grain boundary density, crystallographic orientation, and chemical states in metallic materials. This study comprehensively investigates the fundamental mechanisms and application prospects of GBE in zinc-based anodes, providing pivotal theoretical insights and technical methodologies for designing highly stable electrode architectures. The findings are expected to promote the development of aqueous zinc batteries toward a high energy density and long cycle life. Full article

Zinc–air batteries (ZABs) are crucial for renewable energy conversion and storage due to their cost-effectiveness, excellent safety, and superior cycling stability. However, developing efficient and affordable bifunctional electrocatalysts for the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) at the air cathode remains a significant challenge. Manganese (Mn)-based materials, known for their tunable oxidation states, adaptable crystal structures, and environmental friendliness, are regarded as the most promising candidates. This review systematically summarizes recent advances in Mn-based bifunctional catalysts, concentrating on four primary categories: Mn–N–C electrocatalysts, manganese oxides, manganates, and other Mn-based compounds. By examining the intrinsic merits and limitations of each category, we provide a comprehensive discussion of optimization strategies, which include morphological modulation, structural engineering, carbon hybridization, heterointerface construction, heteroatom doping, and defect engineering, aimed at enhancing catalytic performance. Additionally, we critically address existing challenges and propose future research directions for Mn-based materials in rechargeable ZABs, offering theoretical insights and design principles to advance the development of next-generation energy storage systems. Full article

Accurate prediction of lithium-ion battery capacity degradation under dynamic loads is crucial yet challenging due to limited data availability and high cell-to-cell variability. This study proposes a Latent Gaussian Process (GP) model to forecast the full distribution of capacity fade in the form of high-dimensional histograms, rather than relying on point estimates. The model integrates Principal Component Analysis with GP regression to learn temporal degradation patterns from partial early-cycle data of a target cell, using a fully degraded reference cell. Experiments on the NASA dataset with randomized dynamic load profiles demonstrate that Latent GP enables full-lifecycle capacity distribution prediction using only early-cycle observations. Compared with standard GP, long short-term memory (LSTM), and Monte Carlo Dropout LSTM baselines, it achieves superior accuracy in terms of Kullback–Leibler divergence and mean squared error. Sensitivity analyses further confirm the model’s robustness to input noise and hyperparameter settings, highlighting its potential for practical deployment in real-world battery health prognostics. Full article

The increasing demand for sustainable and renewable energy solutions has made radio frequency energy harvesting (RFEH) a promising technique for powering low-power electronic devices. RFEH captures ambient RF signals from wireless communication systems, such as mobile networks, Wi-Fi, and broadcasting stations, and converts them into usable electrical energy. This approach offers a viable alternative for battery-dependent and hard-to-recharge applications, including streetlights, outdoor night/security lighting, wireless sensor networks, and biomedical body sensor networks. This article provides a comprehensive review of the RFEH techniques, including state-of-the-art rectenna designs, energy conversion efficiency improvements, and multi-band harvesting systems. We present a detailed analysis of recent advancements in RFEH circuits, impedance matching techniques, and integration with emerging technologies such as the Internet of Things (IoT), 5G, and wireless power transfer (WPT). Additionally, this review identifies existing challenges, including low conversion efficiency, unpredictable energy availability, and design limitations for small-scale and embedded systems. A critical assessment of current research gaps is provided, highlighting areas where further development is required to enhance performance and scalability. Finally, constructive recommendations for future opportunities in RFEH are discussed, focusing on advanced materials, AI-driven adaptive harvesting systems, hybrid energy-harvesting techniques, and novel antenna–rectifier architectures. The insights from this study will serve as a valuable resource for researchers and engineers working towards the realization of self-sustaining, battery-free electronic systems. Full article

In this study, we systematically analyzed selective lithium plating on graphite (Gr)–silicon (Si) composite anodes for lithium-ion batteries during fast charging, using electrochemical techniques. To achieve this, half-cells were first constructed with single Gr and Si electrodes, and lithium plating on each electrode was examined at different charging rates. It was observed that lithium plating on both electrodes began at a lower state of charge (SoC) as the charge rate increased. Furthermore, at a given charge rate, lithium plating occurred on the Si electrode at a lower SoC than on the Gr electrode. Based on the experimental findings, the lithium plating behavior of Gr and Si as a function of the charge rate was formulated to investigate the plating behavior of hypothetical composite electrodes with varying Gr–Si ratios. The lithium plating behavior observed on the actual composite electrode was consistent with that predicted from the hypothetical composite electrode, which was simulated using the same Gr–Si ratio based on the behaviors of the individual electrodes. By comparing the results from the single and composite electrodes, it is proposed that lithium plating occurs first on Si and then on Gr at low charge rates, whereas, at high charge rates, it proceeds first on Gr and then on Si. We discuss how to extrapolate the preferential plating signals—namely, plating onto Si at low charge rates and onto Gr at high charge rates—that are not directly evident in the signal from the actual composite electrode. Full article

The increasing prevalence of sedentary lifestyles poses significant global health challenges, including obesity, diabetes, musculoskeletal disorders, and cardiovascular issues. This paper presents the design and development of a universal smart chair system aimed at mitigating the adverse effects of prolonged sitting. The proposed solution integrates a pressure sensor, vibration motors, an LED strip, and Bluetooth Low-Energy (BLE) communication into a modular and adaptable design. Powered by an STM32WB55CGU6 microcontroller and a rechargeable lithium-ion battery system, the smart chair monitors sitting duration and the user’s posture, and provides alerts through tactile, visual, and auditory notifications. A complementary mobile application allows users to customize sitting time thresholds, monitor activity, and assess battery status. Designed for universal compatibility, the system can be adapted to various chair types. Technical and functional testing demonstrated reliable performance, with the chair operating for over eight workdays on a single charge. The smart chair offers an innovative, cost-effective approach to improving workplace ergonomics and health outcomes, with potential for further enhancements such as posture monitoring. A pilot study with 83 students at VILNIUS TECH showed that the smart chair detected correct posture with 94.78% accuracy, and 97.59% of users responded to alerts by adjusting their posture within an average of 3.27 s. Full article