Search Results (1,065)

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a /Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems. Full article

The gut microbiota takes charge in a pivotal role in metabolic equilibrium, immune response, and modulating gut lining stability and has become the main focus of nutrition and functional food research. In this regard, the definition of prebiotics has progressed past the traditional approach limited to indigestible dietary fibers, embracing more targeted, biologically active, and functional delivery systems. In recent years, plant-derived exosomes (PDEs), a subclass of exosomes defined as extracellular vesicles (EVs) in the 30–150 nm size range, have emerged as an innovative class of nanostructures supporting this transformation. Plant-derived exosome-like nanoparticles (PELNs) have been taken into account as natural nanocarriers which are suitable for the gastrointestinal system with the help of their high biocompatibility, low immunogenicity profiles and rich bioactive cargo contents. This review discusses structural features of PELNs, molecular cargo content, and biological roles comprehensively and focuses especially on gut microbiota interactions. MicroRNAs, proteins, lipids, polyphenols, and glycans which PELNs contain are discussed with regard to shaping the microbial composition, regulating microbial metabolic activity, and modulating host-microbe communication. Findings derived from in vitro, in vivo, and limited translational studies indicate that PELNs can modulate specific microbial taxa, increase short-chain fatty acid (SCFA) yield, strengthen mucosal immune homeostasis, and induce source-dependent responses in the gut microbiota. In their traditional definition, prebiotics are taken into account as food components which selectively support proliferation and metabolism of helpful microbes, especially Bifidobacteria and Lactobacilli. Within this framework, PELNs are not only passive carriers of functional components but also evaluated as active systems which can directly affect microbiota composition and metabolic functions. Thus, they are repositioned as “prebiotic nanocarriers.” Also this review evaluates the potential of functional food and integration of major edible PELNs into synbiotic formulations by discussing their isolation and characterization methods and stabilities in the gastrointestinal environment. Limitations of clinical applications and lack of research from a prebiotic nanocarrier perspective of PELNs show that this field still contains important research gaps. The novelty of the study lies in its integration of PELN research with nutrition-based approaches to microbiota modulation and innovative functional food strategies under a single multidisciplinary conceptual framework. Full article

The rapid integration of renewable energy is increasing the need for fast and sustained load-side frequency regulation, and public electric vehicle (EV) charging networks are promising providers. Their participation, however, is constrained by the volatile charging demand and strict service requirements, which make it difficult to balance regulation revenue with charging quality. This paper proposes a three-layer coordinated framework for multi-site charging networks participating in secondary frequency regulation, comprising market bidding, rolling planning, and fast-response tracking. At the market layer, baseline charging schedules are co-optimized with symmetric regulation capacity bids. At the planning layer, completion margin and progress protection constraints are introduced as tractable service safeguards that preserve charging continuity and deadline compliance. At the execution layer, coordinator-assisted distributed station-level tracking and charger-level urgency-aware allocation track automatic generation control (AGC) commands while correcting the charging progress in real time. The station-level problem is decomposed into local box-constrained subproblems coordinated by a scalar dual signal, enabling real-time AGC tracking with limited inter-station information exchange. Case studies on a reproducible simulated network with 20 stations and 600 chargers show that the proposed method improves ancillary service benefits while maintaining strong tracking performance and markedly improving the charging continuity, deadline compliance, and spatial load balance. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

The rapid integration of electric vehicles (EVs) into transportation systems and power grids has significantly increased the complexity of optimization challenges related to routing, charging coordination, scheduling, and energy management. Despite significant research growth, the field remains conceptually fragmented, lacking a unified framework to systematically organize Electric Vehicle Optimization Problems (EVOPs). To address this gap, this study presents a systematic review of 144 peer-reviewed articles published between 2011 and January 2025 and proposes a structured EVOP taxonomy based on problem characteristics and dominant decision variables. The analysis examines mathematical formulations, solution methodologies, and emerging research trends. The results indicate the predominance of metaheuristic methods, while exact techniques are mainly limited to small-scale problems. Additionally, there is a growing trend toward multi-objective and stochastic models that incorporate uncertainty and dynamic decision-making environments. However, challenges remain regarding large-scale validation, standardized benchmarking, and integrated multi-domain modeling. The proposed taxonomy provides a coherent framework that facilitates comparison across optimization domains and supports the development of scalable and intelligent EV management systems. Full article

The current work aims to enhance the structural, optical, photocatalytic, and cytotoxic properties of CuO NPs at varied rGO concentrations of 5% and 10%. In the present work, a one-step hydrothermal method was successfully applied to prepare rGO/CuO NCs at different concentrations of RGO. The novelty of this work was to enhance the structural, optical, photocatalytic, and cytotoxic properties of CuO using the addition of rGO sheets. XRD, TEM, SEM-EDX, XPS, FTIR, UV-vis, PL, and DLS techniques were used to characterize the prepared samples. XRD data confirmed the formation of the monoclinic phase of CuO with a decrease in crystallite size, from 21.14 nm for CuO to 16.94 nm for the 10% rGO/CuO NCs nanocomposite. SEM and TEM images verified the uniform anchoring and excellent dispersion of CuO nanoparticles on the rGO sheets, and the EDX spectra showed the presence of Cu, O, and C elements in the obtained rGO/CuO NCs. DLS measurements showed that the hydrodynamic radius dropped from 69.98 ± 17.81 nm for CuO to 51.72 ± 10.48 nm for 10% rGO/CuO NCs. The zeta potential values remained negative for all samples, ranging from −20.50 ± 8.69 mV for CuO to −25.60 ± 9.08 mV for 10% rGO/CuO NCs, suggesting enhanced colloidal stability with rGO incorporation. Furthermore, FTIR and XPS analyses confirmed that Cu–O–C bonding formed between CuO and rGO. UV-Vis analysis revealed a redshift in the absorption edges as rGO content increased, reducing the band gap from 3.65 eV to 3.60 eV. Additionally, PL spectra showed a marked reduction in emission intensity due to a decrease in the recombination rate between electron (e⁻)–holes (h⁺) pairs. The CuO/(10%)rGO NCs showed the best photocatalytic performance with a 93.56% degradation of methylene blue (MB) after 120 min under UV irradiation, and followed pseudo-first-order kinetics with k = 0.0203 min⁻¹. Cytotoxicity studies on HT1080 cells showed a dose-dependent decrease in viability. 10% rGO/CuO NCs exhibited the highest cytotoxicity effect, resulting in 58% and 50% viability at 1.4 mg/mL, respectively. The presented results showed that the presence of rGO in CuO NPs played a role in enhancing the structural stability, charge mobility, and biological reactivity of Cu NPs. This study highlighted that the rGO/CuO NCs are a promising multi-functional material for environmental and biomedical applications. Full article

Electric Vehicle Charging Systems (EVCSs) are increasingly connected with the Internet of Things (IoT) and smart grid infrastructure, yet they face growing cyber risks due to expanded attack interfaces. These systems are vulnerable to various attacks that potentially impact both charging operations and user privacy. Intrusion Detection Systems (IDSs) are essential for identifying suspicious activities and mitigating risks to protect EVCS networks, but conventional ML-based IDSs are often unable to achieve optimal performance due to imbalanced datasets, complex traffic distributions, and human design limitations. In practice, EVCS traffic is typically multi-class, imbalanced, and safety-critical, where both missed attacks and false alarms can lead to denial of charging, service interruption, unnecessary incident escalation, financial loss, and reduced user trust. Automated ML (AutoML) and Generative Artificial Intelligence (GAI) have emerged as promising solutions in cybersecurity. Existing GAI and augmentation methods are mostly class-frequency-driven, but this does not necessarily improve the error-prone regions where IDSs actually fail. In this paper, we propose a GAI and an AutoML-based IDS that incorporates a Conditional Generative Adversarial Network (cGAN) with the optimized XGBoost model to improve the effectiveness of intrusion detection in EVCS networks and IoT systems. The proposed framework involves two techniques: (1) a novel cGAN-based error-guided generative augmentation (EGGA) method that extracts misclassified samples and generates a more robust training set for IDS development, and (2) an optimized IDS model that automatically constructs an optimized XGBoost model based on Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE). The main algorithmic novelty lies in EGGA, which uses model errors to guide generative augmentation toward difficult decision regions, while the overall pipeline represents a practical system-level integration of EGGA, XGBoost, and BO-TPE. To the best of our knowledge, this is the first work that combines GAI and AutoML to specifically improve detection on hard samples, enabling more autonomous and reliable identification of diverse cyber attacks in EV charging networks and IoT systems. Experiments are conducted on two benchmark EVCS and cybersecurity datasets, CICEVSE2024 and CICIDS2017, demonstrating consistent and statistically meaningful improvements over state-of-the-art IDS models. This research highlights the importance of combining automation, generative balancing, and optimized learning to strengthen cybersecurity solutions for EV charging networks and IoT systems. Full article

Conventional black-box models for electric vehicle (EV) battery State-of-Health (SOH) prediction achieve high accuracy but lack interpretability, limiting their practical deployment in Battery Management Systems (BMSs). To circumvent these limitations, this study proposes a novel Grey-Box Residual-Driven Framework (GBRDF) that synergizes Deep Symbolic Regression (DSR) with a residual-learning BiLSTM network with two contributions: (1) the DSR component derives explicit, interpretable mathematical expressions governing global degradation trajectories based on electrochemical features, and (2) the BiLSTM network models the residual errors to capture high-frequency nonlinearities and complex sequential dependencies not addressed by the symbolic baseline. By fusing the physics-informed transparency of DSR with the data-driven refinement of BiLSTM, the GBRDF significantly enhances forecasting precision. Experimental validation across four independent EV datasets shows that the GBRDF achieves the highest coefficient of determination (R²) of 0.982, and the lowest mean absolute error (MAE) of 0.1398 and root mean square error (RMSE) of 0.3176, significantly outperforming existing methods. Furthermore, the DSR-derived SOH equation shows that battery degradation is primarily driven by high voltage exposure and charging time, with mathematical transformations reflecting how degradation accelerates initially then slows, matching real-world aging patterns where voltage stress dominates over temperature and usage variations. Full article

In this study, TiO₂ nanoparticles (NPs), CdS NPs and TiO₂/CdS nanocomposite were synthesized via the sol–gel, hydrothermal and ex situ method, respectively. The synthesized materials were characterized using XRD, UV–vis DRS, FTIR, SEM, and EDX analysis. XRD analysis confirmed the crystalline structure of the as-prepared samples, while the bandgap energy of TiO₂ NPs, CdS NPs, and TiO₂/CdS nanocomposite were determined to be 2.98, 1.94, and 2.27 eV, respectively. Photocatalytic efficiency of TiO₂ NPs, CdS NPs, and TiO₂/CdS nanocomposite was systematically evaluated by photocatalytic degradation of crystal violet (CV) dye under visible-light irradiation. Under optimized reaction conditions of [CV concentration] = 20 mg/L, [catalyst dosage] = 0.25 g/L, and pH = 6, TiO₂/CdS nanocomposite achieved 86.3% removal of CV within 180 min, outperforming pure TiO₂ NPs (16.4%) and CdS NPs (66.9%). The enhanced performance of TiO₂/CdS nanocomposite as compared to CdS NPs is attributed to improved charge separation via heterojunction formation, while significantly superior performance over TiO₂ demonstrates successful visible-light activation. Further optimization study revealed that maximum removal efficiency of CV (97.1%) was achieved at lower dye concentration (10 mg/L). Photocatalytic degradation of CV followed pseudo-first-order kinetics. Moreover, scavenger experiments confirmed hydroxyl radicals (^•OH) as dominant reactive species. Furthermore, the TiO₂/CdS nanocomposite demonstrated good reusability with minimal activity loss after five runs. Additionally, the as-prepared nanocomposites showed significant antibacterial activity against Pseudomonas aeruginosa (P. aeruginosa). The present study indicated that TiO₂/CdS nanocomposite could be simultaneously used for degradation of organic pollutants as well as for removal of microorganisms while targeting environmental sustainability and water purification. Full article

To reduce charging time and improve operational efficiency at integrated energy stations (IESs) for electric vehicles (EVs), this paper develops a sustainability-oriented collaborative optimization model by coupling vehicle routing behavior with charging decision-making. Firstly, a dynamic road network model is established to simulate vehicle arrivals at IESs from different network nodes. Then, considering grid peak–valley electricity prices, station electricity procurement costs and EV charging demand, a dynamic pricing strategy for IESs is proposed to guide EVs to charge at off-peak hours so as to realize peak shaving and valley filling for the power grid. Meanwhile, the NSGA-III algorithm is improved through the introduction of Good Point Set initialization and an adaptive crossover mechanism, and the Good Point Set initialization and Adaptive Crossover NSGA-III (GPS-AC-NSGA-III) algorithm is proposed to solve the scheduling optimization problem. Finally, the CRITIC-based TOPSIS method is employed to identify the optimal compromise solution from the Pareto-optimal set. Case studies further prove the effectiveness of the proposed multi-objective collaborative optimization model for EVs and IESs. Compared with scenarios without dynamic Dijkstra-based navigation and dynamic pricing, the IES daily revenue increased by 39.83%, pollutant emissions decreased by 0.4%, and the peak-to-valley load difference ratio was reduced by 4.94%. The results indicate that dynamic Dijkstra-based vehicle routing improves travel efficiency, while the proposed dynamic pricing strategy enhances station profitability and smooths grid load fluctuations. Overall, the proposed framework contributes to sustainable transportation and energy systems by reducing pollutant emissions, improving energy efficiency, and enhancing the operational stability of integrated energy infrastructure, thereby supporting the transition toward low-carbon and sustainable urban energy systems. Full article

Under China’s “Dual Carbon” goals, the electric vehicle (EV) industry has expanded rapidly, while the imbalance between supply and demand in public charging infrastructure (PCI) has emerged as a critical bottleneck. Accordingly, a structural assessment of PCI demand potential is essential for improving planning effectiveness. Focusing on the seven municipal districts of Qingdao, this study developed a dual-dimensional framework integrating physical space and population activity. Five core factors were incorporated: road network accessibility, road network betweenness, POI functional mixing density, population distribution density, and nighttime light intensity. By integrating Spatial Design Network Analysis (sDNA), Kernel Density Estimation (KDE), and the entropy weighting method, we conducted a structural assessment of PCI demand potential and derived spatial demand tiers and hierarchy. The results indicate that: (1) road network betweenness had the highest weight (0.396), acting as the dominant driver of structural demand potential, followed by POI functional mixing density (0.271), whereas nighttime light intensity (0.151) and population distribution density (0.143) functioned as baseline supportive indicators; (2) spatial demand was classified into five levels (Levels 1–5), with Level 1 hotspots exhibiting a radial spatial structure characterized by “one primary core, four secondary cores, three corridors, and multiple nodes”; and (3) while the existing PCI distribution exhibited overall gradient consistency with the structurally derived demand tiers, quantitative deviation results indicated localized mismatches, including under-allocation in high-demand areas and over-allocation in selected lower-demand pockets. The proposed dual-dimensional framework facilitates the identification of structural demand gradients for PCI by explicitly incorporating traffic-flow potential, functional aggregation, and population concentration. These findings provide planning-oriented diagnostic support for PCI configuration and contribute to the sustainable transformation of urban transportation systems in megacities. Full article

Efficient charge transfer and effective separation of photo-generated charge carriers are pivotal to the photocatalytic process. In this study, a novel CoAl₂O₄@nitrogen-doped graphitic carbon (CoAl₂O₄@NGC) composite photocatalyst was fabricated via a stepwise hydrothermal method coupled with high-temperature calcination, and its photocatalytic performance for CO₂ reduction was systematically investigated. X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and photoelectrochemical measurements were employed to characterize the phase structure, microstructure, surface chemical state and photoelectrochemical properties of the catalyst. Spinel-structured CoAl₂O₄ nanoparticles were uniformly anchored on the NGC substrate, forming a well-integrated composite interface. XPS analysis confirmed the coexistence of Co²⁺/Co³⁺ mixed valence states in CoAl₂O₄ which provides abundant redox sites for CO₂ activation. Photocatalytic tests showed that CoAl₂O₄@NGC exhibits excellent catalytic activity and cycling stability, with CO and CH₄ yields of 27.88 μmol·g⁻¹·h⁻¹ and 23.90 μmol·g⁻¹·h⁻¹, respectively. The narrow bandgap (1.54 eV) enhances visible light absorption, while efficient electron-hole separation and reduced charge transfer resistance improve photocatalytic efficiency. Theoretical calculations further reveal that CoAl₂O₄@NGC lowers the adsorption free energy of CO₂ and the energy barrier for COOH formation, thus facilitating the photocatalytic CO₂ reduction. This work provides insights for the design of efficient and stable photocatalysts for CO₂ reduction and deepens the understanding of the synergistic catalytic mechanism in the spinel/nitrogen-doped carbon composite system. Full article

To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module is employed to capture the multi-scale spatiotemporal features of the ETCN. The topological changes and energy-information interaction characteristics under fault scenarios are analyzed. Second, a Finite Markov Decision Process (FMDP) framework is established to address the stochastic and dynamic nature of EV charging behavior. The charging station selection and route planning problem is transformed into an agent decision-making process. A reward function is designed by incorporating voltage constraints, traffic flow constraints, and state-of-charge margin penalties. This ensures a balanced consideration of power grid security and traffic efficiency. The FMDP model is then solved using a Deep Q-Network (DQN) to achieve optimal EV charging guidance under fault conditions. Finally, case studies are conducted on a coupled simulation scenario consisting of an IEEE 33-node power distribution system and a 23-node transportation network. Results show that the proposed method reduces the system operation cost to 218,000 CNY, controls the voltage deviation rate of the distribution network at 3.1% in line with the operation standard, and enables the model to achieve stable convergence after only 250 training episodes. It can effectively optimize the charging load distribution and maintain the voltage stability of the power grid under fault conditions. Full article

Nd₂NiO_4+δ was investigated as a Ruddlesden–Popper (RP) cathode material for intermediate-temperature solid oxide fuel cells (IT-SOFCs), with particular emphasis on its electrochemical performance and oxygen reduction reaction mechanism. The material was synthesized via a polymeric sol–gel route derived from Pechini’s method and evaluated in symmetric cells using Ce_0.9Gd_0.1O_2−δ (GDC) as the electrolyte. X-ray diffraction confirmed the formation of a single RP phase and good chemical compatibility with GDC after thermal treatments at 800 °C. Cathode layers with thicknesses of 8–12 µm were deposited by dip-coating. Electrical conductivity measurements revealed a thermally activated semiconducting behavior governed by Ni²⁺/Ni³⁺ small-polaron hopping, with an activation energy of ~1.08 eV. Electrochemical impedance spectroscopy showed a strong temperature dependence of the area-specific resistance, decreasing from 9.18 Ω·cm² at 600 °C to 0.39 Ω·cm² at 800 °C. Distribution of relaxation times (DRT) analysis enabled the identification of the dominant electrochemical processes, indicating that oxygen surface exchange reactions are more favorable than charge transfer at the cathode–electrolyte interface, which remains the main limiting step. These results demonstrate that Nd₂NiO_4+δ is a promising cathode for IT-SOFC operation, while further optimization of the electrode–electrolyte interface is required to enhance its oxygen reduction kinetics. Full article

The development of efficient, stable, and sustainably fabricated photocatalysts for solar-driven hydrogen evolution remains a critical challenge in the field. Herein, we report a novel green coprecipitation strategy to synthesize calcium-doped zinc oxide (Ca-ZnO) nanosheets, utilizing cactus juice as a natural, multifunctional medium for the coprecipitation process. This method enables the in situ, tunable incorporation of 3–7% Ca²⁺ ions into the wurtzite ZnO lattice without the use of harsh chemical reagents. Comprehensive characterization confirms that Ca²⁺ substitutionally replaces Zn²⁺, which preserves the intrinsic crystal structure of ZnO well while inducing the formation of uniform nanosheet morphology. This doping strategy effectively modulates the electronic band structure, progressively narrowing the bandgap from 3.19 eV to 2.90 eV and significantly enhancing visible-light absorption. Crucially, the incorporation of Ca²⁺ also generates oxygen vacancies, which serve as efficient electron traps to suppress photogenerated charge carrier recombination. The optimized 5%Ca-ZnO photocatalyst demonstrates a favorable hydrogen evolution rate of 889 μmol·g⁻¹·h⁻¹ under full-spectrum irradiation, with stability, retaining 94.8% of its activity after four cycles. This work not only provides a high-performance material but also establishes a generalizable, sustainable paradigm for the design of advanced semiconductor photocatalysts. Full article