Search Results (42)

N-nitrosamine compounds, a disinfection byproduct of chlorination and chloramination in water and wastewater treatment processes, are classified as a probable human carcinogen. The current review focuses on analysing the feasibility of membrane technology while examining the challenges and opportunities in the elimination of N-nitrosamine compounds, particularly NDMA, from wastewater. To systematically attain this goal, this paper uses a systematic literature review that screens and critically assesses peer-reviewed experimental and numerical published papers on N-nitrosamine removal, occasioning in 37 high-quality papers for synthesis. In this regard, a detailed analysis of experimental and numerical studies elaborates that conventional RO membranes often introduce a specific low removal of NDMA from wastewater due to their low molecular weight and neutral charge, which addresses a critical issue. The critical analysis of the experimental and numerical studies depicts that the membrane type, structural properties, and chemical interaction have a key role in the removal of NDMA. To systematically improve the NDMA removal, a wide set of investigations have explored innovative treatment methods, including Nano pore plugging and hydrophilic coatings. This demonstrates potential for improving NDMA removal, albeit at the penalty of reduced water permeability. Additionally, the heat treatment of membranes has attained a notable improvement, ensuing in NDMA rejection of up to 92%. A multi-stage RO configuration model has depicted a maximum NDMA rejection of 93.1%. The future research should focus on investigating possible improvement of NDMA removal from wastewater such as Nano pore plugging and hydrophilic coatings, besides optimising RO configurations and membrane designs with a deeper understanding of membrane fouling. Full article

As the number of electric vehicles (EV) increases, the demand for recommending the best charging location when using a large-scale charge network to charge is also increasing. A successful recommendation will utilize the user’s preference and the operational constraints of the charging network to make sure that it also takes into account the real-time operational requirements of the network. Most current papers focus on optimizing individual algorithmic components in isolation; consequently, many of these papers neglect to provide a holistic view of an integrated system. In addition, there are many operational requirements that current research does not consider, such as cold-start personalization for new users and enforcing real-time operational constraints like station availability, power capacity, maintenance windows, etc. This paper describes a deployable multi-stage recommendation system that creates a candidate list based on location and ranks preferences based on user, station and context features. The recommendation system also adds a configurable rule-based re-ranking layer to ensure that both hard constraints (i.e., charger availability and power-cap limits) and soft objectives (i.e., load balancing and operator priority) are enforced. A method for enabling mixed use between stable Bayesian and adaptive Bayesian methods was developed to provide users starting with cold-start performance that do not have adequate histories. Evaluation of this method using 100k+ real charging sessions showed that the fraction of sessions where the ground-truth station appears in the top-two recommendations (Hit@2) for the recommendation system was 0.82, representing a 37% increase in performance compared to proximity-based recommendation methods. The online deployed recommendation system has a 99th-percentile serving latency (P99) of less than 200 ms. The findings of this paper provide a framework for the implementation of operationally-relevant user-centric recommendation systems for EV services at scale. Full article

Battery ageing is inevitable during operation, leading not only to performance degradation but to potential safety concerns. Consequently, accurate prediction of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safety and reliability. This study proposed a novel hybrid neural network architecture that integrates a transformer module, an empirical degradation (ED) model, and a gated recurrent unit (GRU). The transformer module enhances the global representation of the feature sequence, while the ED model comprehensively considers the impact of temperature on the rate of battery capacity degradation, compensating the un-interpretability of the transformer architecture in predicting SOH. In addition, pseudo-incremental capacity curves have been obtained using charging fragments from multi-stage constant current fast charging, which solves the issue of extracting mechanism features under fast charging conditions. Experimental results demonstrate that, across a wide temperature range, the model maintains a low average RMSE between 0.43% and 0.59% for prediction horizons of 4 to 128 cycles. Specifically, the average RMSE is 0.87% at −5 °C and 0.37% between 25 °C and 55 °C. Compared to standalone data-driven models, the proposed hybrid architecture reduces prediction error by approximately 50% at 25 °C, exhibiting superior predictive performance and robustness. Full article

Triboelectric nanogenerators (TENGs) have emerged as efficient mechanical-energy harvesters with advantages—simple architectures, broad material compatibility, low cost, and strong environmental tolerance—positioning them as key enablers of self-powered systems. This review synthesizes recent progress in energy-storage interfaces, power management, and system-level integration for TENGs. We analyze how intrinsic source characteristics—high output voltage, low current, large internal impedance, and pulsed waveforms—complicate efficient charge extraction and utilization. Accordingly, this work highlights a variety of power-conditioning approaches, including advanced rectification, multistage buffering, impedance transformation/matching, and voltage regulation. Moreover, recent developments in the integration of TENGs with storage elements, cover hybrid topologies and flexible architectures. Application case studies in wearable electronics, environmental monitoring, smart-home security, and human–machine interfaces illustrate the dual roles of TENGs as power sources and self-driven sensors. Finally, we outline research priorities: miniaturized and integrated power-management circuits, AI-assisted adaptive control, multimodal hybrid storage platforms, load-adaptive power delivery, and flexible, biocompatible encapsulation. Overall, this review provides a consolidated view of state-of-the-art TENG-based self-powered systems and practical guidance toward real-world deployment. Full article

In lithium-ion battery charging, balancing charging speed with efficiency and state of health (SOH) is paramount. First, a multi-state electric-thermal-aging coupling model was developed to accurately reflect battery operating conditions. Second, a voltage-based multi-stage constant current-constant voltage (VMCC-CV) strategy was implemented, incorporating an innovative V-SOC-R_int conversion mechanism—integrating voltage, state of charge (SOC), and internal resistance—to effectively mitigate thermal buildup during transitions. To optimize the VMCC-CV currents, an innovative enhancement was applied to the moth-flame optimization (MFO) algorithm, demonstrating superior performance over its traditional counterpart across diverse charging scenarios. Finally, three practical strategies were devised: rapid charging, multi-objective balanced charging, and enhanced safety performance charging. Relative to the manufacturer’s 0.75 C-CCCV protocol, the balanced strategy significantly accelerates charging, reducing time by 34.11%, while sustaining 93.54% efficiency and limiting SOH degradation to 0.006856%. Compared to conventional CCCV methods, the proposed approach offers greater versatility and applicability in varied real-world scenarios. Full article

A fundamental challenge in lithium-ion battery charging is the inherent trade–off between charging speed and battery health. Fast charging tends to accelerate battery degradation, while slow charging extends downtime and intensifies range anxiety, heightening concerns over inadequate driving range during operation. This contradiction has become a key bottleneck restricting the advancement of electric vehicles. In response to the limitations of conventional charging strategies and optimization methods, which typically intensify this trade–off, this study proposes a novel two–stage fast charging optimization strategy for lithium–ion batteries. The proposed method first introduces a hybrid clustering algorithm that combines the canopy algorithm with bisecting K–means to achieve adaptive SOC staging. This staging is guided by the nonlinear characteristics of the internal resistance with respect to the state of charge (SOC), allowing for a data–driven division of charging phases. Following staging, a closed–loop optimization framework is developed. A wavelet neural network (WNN) is employed to precisely capture and approximate the nonlinear characteristics of the charging process for performance prediction, upon which a multi–strategy enhanced multi–objective particle swarm optimization (MOPSO) algorithm is applied to efficiently search for Pareto–optimal solutions that balance charging time and ohmic loss. In addition, an active learning mechanism is incorporated to refine the WNN using selectively sampled data iteratively, thereby improving prediction accuracy and the robustness of the optimization process. Experimental results demonstrate that when the SOC reaches 70%, the proposed method shortens the charging time by 12.5% and reduces ohmic loss by 31% compared with the conventional constant current–constant voltage (CC–CV) strategy, effectively achieving a balance between charging efficiency and battery health. Full article

The potential of silicon carbide (SiC) as a promising high-capacity and stable anode material is hindered by poor electronic conductivity and slow lithium diffusion kinetics. Here, we report a one-step laser chemical vapor deposition (LCVD) process to directly synthesize porous graphene@SiC heterostructures on carbon fiber substrates. This in situ method yields an integral, binder-free electrode architecture that enhances mechanical robustness against pulverization. A critical feature of this heterostructure is the built-in electric field at the graphene–SiC interface, which is revealed by theoretical calculations to significantly accelerate charge transport and lithium-ion diffusion. The resulting anode delivers a high reversible capacity of 668 mAh·g⁻¹ after 100 cycles at 0.1 A·g⁻¹. More remarkably, a unique multi-stage activation mechanism is discovered, leading to an unprecedented capacity rebound to 735 mAh·g⁻¹ after cycling at rates up to 5 A·g⁻¹. This activation process is observed to accelerate with increasing current density in the 0.1–2 A·g⁻¹ range. Furthermore, post-cycling analysis via XRD, TEM, and XPS confirms both the structural durability of the electrode and a reversible lithium intercalation mechanism, providing a critical foundation for the future design of high-performance LIB anodes. Full article

As electric vehicles (EVs) continue to advance toward widespread adoption, innovations in power electronics are playing a pivotal role in improving efficiency, performance, and sustainability. This review presents recent progress in bidirectional converters and regenerative braking systems (RBSs), highlighting their contributions to energy recovery, battery longevity, and vehicle-to-grid integration. Bidirectional converters support two-way energy flow, enabling efficient regenerative braking and advanced charging capabilities. The integration of wide-bandgap semiconductors, such as silicon carbide and gallium nitride, further enhances power density and thermal performance. The paper evaluates various converter topologies, including single-stage and multi-stage architectures, and assesses their suitability for high-voltage EV platforms. Intelligent control strategies, including fuzzy logic, neural networks, and sliding mode control, are discussed for optimizing braking force and maximizing energy recuperation. In addition, the paper explores the influence of regenerative braking on battery degradation and presents hybrid energy storage systems and AI-based methods as mitigation strategies. Special emphasis is placed on the integration of RBSs in advanced electric vehicle platforms, including autonomous systems. The review concludes by identifying current challenges, emerging trends, and key design considerations to inform future research and practical implementation in electric vehicle energy systems. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

With the proliferation of electric vehicles (EVs), vehicle-to-vehicle (V2V) energy transfer has emerged as a critical technology for dynamic energy complementarity. This technology addresses “range anxiety”, thereby supporting carbon neutrality goals through the enhanced utilization of renewable-powered EVs. In order to achieve fast, safe V2V charging and improve device portability, it is necessary to optimize the charging mode and simplify the device. Therefore, this paper proposes a hierarchical control strategy for constant power (CP) charging in a V2V device with a dual-active-bridge (DAB) converter topology. First, different from traditional constant voltage (CV) and constant current (CC) charging, a unified nonlinear DAB model integrating CV/CP/CC charging modes is proposed. Furthermore, sensorless current estimation based on finite-time disturbance observers further reduced the size of the device. Finally, a hierarchical control architecture was constructed by combining backstepping control theory, which ensures global stability of multi-stage charging processes through the dynamic adjustment of phase-shift ratios. The effectiveness of the proposed methodology was validated through simulation and hardware-in-the-loop experimental results. Full article

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article

Extended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap by developing a novel parking charging strategy for E-RHEVs that leverages a temperature-dependent battery aging model and a Multi-Objective Mantis Search Algorithm (MOMSA)—a metaheuristic optimization algorithm designed to solve multi-objective problems by efficiently exploring trade-offs between conflicting objectives. The MOMSA optimizes a five-stage State-of-Charge-based Multi-stage Constant Current (SMCC) charging profile—a dynamic current adjustment strategy that minimizes battery capacity degradation by dividing the charging process into sequential phases. The MOMSA-based SMCC strategy achieves an optimal balance between charging time and battery capacity degradation across a range of ambient temperatures (5 °C to 35 °C). Compared to a conventional 0.5C CC-CV charging strategy, the MOMSA-based SMCC strategy demonstrably reduces battery degradation with a moderate increase in charging time. Furthermore, the MOMSA-based charging strategy outperforms a Multi-Objective Particle Swarm Optimization (MOPSO)-based approach, achieving comparable degradation mitigation while significantly reducing charging time. One-week cycling simulations under realistic driving conditions further validate the MOMSA-based charging strategy’s superior long-term performance in delaying battery degradation across various temperatures. This strategy extends E-RHEV battery lifespan while maintaining operational efficiency. Full article

This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from the offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to metaheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that incorporates both charging time and charging temperature was constructed to facilitate the use of DO in obtaining the OCP. To verify the performance of the proposed method, various algorithms, including the constant current-constant voltage (CC-CV) technique, formula method (FM), particle swarm optimization (PSO), war strategy optimization (WSO), jellyfish search algorithm (JSA), grey wolf optimization (GWO), beluga whale optimization (BWO), levy flight distribution algorithm (LFDA), and African gorilla troops optimizer (AGTO), were introduced. Based on the OCP extracted from the simulations using these MOAs for the specified ECM model, a charging experiment was conducted on the Panasonic NCR18650PF LiB to evaluate the charging performance in terms of charging time, temperature, and efficiency. The results demonstrate that the proposed DO technique offers superior charging performance compared to other charging methods. Full article

This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%. Full article

Lithium-ion batteries are essential for the development of consumer electronics and electric vehicles due to their high energy density, low self-discharge rate, and easy maintenance. To optimize the performance of lithium-ion batteries and meet the battery requirements of devices, it is necessary to charge the batteries at a faster rate. Therefore, this paper proposes a five-stage constant current charging method based on Lamb wave depolarization to enhance the charging efficiency. Specifically, the orthogonal experimental method is first used to determine the near-optimal value of the charging current in each stage of the five-stage constant current charging process. Subsequently, Lamb waves are introduced during the charging process of each constant current charging stage. Compared with the traditional five-stage constant current charging method, the five-stage constant current charging method based on Lamb wave depolarization improves the charging efficiency. The charging efficiency of the five-stage constant current charging method based on Lamb wave depolarization with an excitation voltage peak-to-peak amplitude Vpp of 120 and an excitation duration of 6 min is 20% higher than that of the traditional five-stage constant current charging method. The weakening of the polarization effect is positively correlated with the Lamb wave excitation voltage. In addition, the five-stage constant current charging method based on Lamb wave depolarization is superior to the five-stage constant current shelving depolarization charging method and the five-stage constant current negative pulse depolarization charging method in improving the charging efficiency. Full article