Search Results (750)

Against the backdrop of in-depth integration between the digital economy and green low-carbon development, exploring how digital technologies facilitate the systematic low-carbon transition of economy and society bears profound theoretical and practical implications for accomplishing China’s “Dual Carbon” goals. Based on provincial-level panel data covering 31 Chinese provinces over the period from 2015 to 2024, this paper adopts two-way fixed-effect specification, instrumental variable approach and Bootstrap-based mediation test to empirically identify the causal impact, underlying mechanisms and heterogeneous boundary conditions of digital technology adoption on low-carbon economic transition. The empirical results demonstrate three core findings. First, digital technology applications exert a statistically significant positive effect on low-carbon economic transition, and this benchmark result remains robust after a battery of robustness tests and endogenous bias corrections. Second, the mechanism estimation uncovers a sophisticated transmission pathway: digital technologies directly accelerate low-carbon transition, yet generate an adverse indirect impact via the green investment channel, which jointly forms a suppressing effect in the mediation framework. Third, the enabling effect of digital technologies on decarbonization presents striking regional imbalance, with significant promotional effects concentrated exclusively in eastern provinces and regions featuring well-developed marketization, which highlights the indispensable moderating role of regional endowments and institutional environments. This study contributes novel empirical evidence to unpack the intricate nexus between digital advancement and green transition, and delivers actionable policy references for designing differentiated and coordinated strategies to integrate digital upgrading with low-carbon development. Full article

In nitrate electrochemical reduction reaction (NO₃RR), competing side reactions like hydrogen evolution often lead to poor selectivity and subpar kinetics, limiting practical use. Herein, using iron oxyhydroxide nanoarrays grown on a titanium mesh as the substrate, silver nanoparticles were introduced onto the tips of the iron oxyhydroxide nanowires via electrochemical deposition, thereby forming an Ag/FeOOH heterojunction electrocatalyst. At −0.85 V, Ag/FeOOH demonstrates excellent performance, with 97.56% ammonium selectivity, 92.45% nitrate conversion rate, and an ammonium yield of 3.21 mg h⁻¹ cm⁻². Furthermore, the Zn-NO₃⁻ battery exhibited a power density of 1.28 mW cm⁻². Ag/FeOOH’s structure enhances interfacial nitrate adsorption and reduces NO₃RR energy barriers, accelerating reaction kinetics. It promotes NO₃⁻-to-NO₂⁻ conversion via dual-site synergy, boosting NH₄⁺ yield and advancing electrocatalyst design. Full article

This study presents a novel iterative dual-loop methodology for the technical sizing of DC-coupled PV-BESS systems. The method was implemented for a commercial broiler farm characterized by a highly variable electricity demand profile (annual consumption: 7.6 MWh; coefficient of variation: 53%). The methodology introduces two original energy utilization indicators—the photovoltaic-to-converter matching factor (W_{PV_S}) and the photovoltaic-to-BESS matching factor (W_{PV_B})—enabling purely technical optimization independent of economic conditions. Minimization of the radius of curvature of the WPVB characteristic curve is applied as a rigorous mathematical criterion for determining the optimal BESS capacity. Simulation results indicate that the optimal configuration consists of a 9.7 kWp photovoltaic system, a 7 kW DC converter, and a 15 kWh battery storage system. The integration of an optimally sized energy storage system increased the self-consumption coverage ratio from 38% to 59% and improved the photovoltaic energy utilization factor from 35% to 54%. Additional economic analysis demonstrates that the PV-only subsystem achieves a simple payback period ranging from 8 to 18 years, depending on the selected pricing scenario. Consequently, the technically optimal configuration identified using the proposed methodology represents a practically feasible investment for broiler production facilities operating under Polish net-billing conditions. The proposed methodology provides a reproducible, economically independent framework for the design of DC-coupled PV-BESS systems in agricultural prosumer facilities, addressing a critical gap in the optimization literature and offering practical sizing guidelines applicable to similarly high-variability load profiles. Full article

The increasing adoption of electric vehicles (EVs) demands highly efficient bidirectional DC–DC converters capable of seamless energy transfer between the grid and vehicle batteries. This paper introduces a Fixed-Frequency Dual-Active-Bridge (DAB) resonant converter featuring four degrees of freedom, achieved through a combination of triple phase-shift (TPS) modulation and a current-controlled variable inductor (VI). The proposed control strategy aims to minimize conduction and switching losses by simultaneously managing reactive power, RMS current, and soft-switching conditions across wide variations in voltage and power. Unlike conventional phase-shift or variable-frequency modulations, the fixed-frequency operation maintains full zero-voltage switching (ZVS) for the two bridges, and zero-current switching (ZCS) in the bridge that is receiving energy, enhancing overall system reliability and control simplicity. The proposed converter is validated through simulations and experimental results from a SiC MOSFET-based 14 kW prototype operating at 122 kHz, demonstrating peak efficiencies above 97% under both charging and discharging modes. The experimental results confirm that the proposed DAB topology and modulation scheme significantly improve efficiency and controllability, making it a promising solution for next-generation on-board chargers and vehicle-to-grid (V2G) applications. Full article

Microwave wireless power transfer (MWPT) is a promising technology for powering dedicated industrial Internet of Things (IoT) devices, enabling battery-free operation. However, in realistic MWPT deployments, the received RF signals fluctuate drastically due to varying transmission distances and multipath fading. Additionally, the equivalent impedance of sensor nodes varies significantly during duty cycles, shifting between a low-resistance active state and a high-resistance sleep state. Consequently, maintaining high rectification efficiency under these dynamic conditions remains a critical challenge. This paper proposes a high-efficiency rectifier with a wide input power and load range based on the suppression of second and third harmonics. The rectifier adopts a dual-diode parallel configuration. By leveraging the impedance compensation characteristics of two short-circuited stubs with distinct electrical lengths, it simultaneously achieves fundamental-frequency impedance matching and harmonic suppression without the need for an additional matching network. Validated through theoretical derivation, simulation analysis, and physical prototype testing, the proposed 2.45 GHz rectifier realizes high-efficiency rectification over a wide dynamic range. Experimental results demonstrate that the power dynamic range reaches 10 dB when the rectification efficiency exceeds 70%, and extends to 17 dB when the efficiency is above 60%. Furthermore, the rectification efficiency is insensitive to load variations (100–1200 Ω), making it highly suitable for powering wireless sensor nodes with varying operating modes in complex electromagnetic environments. Full article

Electric vehicles (EVs) relying solely on lithium-ion (Li-ion) batteries face limitations related to range, mass, charging time, and battery downsizing. This study develops a dynamic system-level modeling framework for integrating an aluminum–air (Al–air) battery with a Li-ion traction battery within a MATLAB/Simulink electric vehicle platform. Two integration strategies were evaluated: (i) Al–air operation as a range extender activated through SOC-based control logic, and (ii) Al–air operation as an auxiliary power unit supplying non-traction loads. The Al–air subsystem was implemented using an experimentally informed polarization-based model coupled with aluminum consumption tracking and DC–DC converter integration. Vehicle performance was evaluated under UDDS, HWFET, WLTP, and FTP-75 drive cycles. Results show that coupling a 24.6 kWh Al–air pack with a downsized 20.3 kWh Li-ion pack enabled driving ranges of 379 km (UDDS), 523 km (HWFET), and 450 km (WLTP), exceeding the baseline full-capacity Li-ion configuration while reducing total battery-system mass by more than 50%. When operated as an auxiliary power unit under a constant 3 kW auxiliary load, the Al–air system increased the vehicle range by 44–96 km depending on the drive cycle. The results demonstrate the feasibility of Al–air-assisted dual-energy-storage architectures for extending the EV range while reducing dependence on large Li-ion battery packs. Full article

Experimental validation delivers five quantified outcomes. First, optical pheromone detection achieves 88.7% ± 0.6% accuracy (n = 150, 95% CI), and the dual-modality combined channel achieves 86.1% ± 0.9% (n = 200), with robustness confirmed under 50/60 Hz flicker interference, rapid 200–1200 lux light transitions (485 ms settling), and reflective glare spots. Second, the MQ-135 chemical channel calibration holds R² ≥ 0.999 across temperatures of 15–35 °C and humidity of 30–90%, with maximum voltage drift of 0.093 V at the highest temperature. Third, 3.2× CNN inference speedup through 8-bit quantisation runs at 15 FPS within 1.8 W. Fourth, peripheral subsystems draw a measured mean of 1.19 W ± 0.02 W (n = 60, 95% CI); the complete per-robot system, including the Jetson Orin Nano compute rail, draws 6.15 W ± 0.09 W, enabling six-hour missions from the 55.08 Wh battery. Fifth, localisation across ten trials yields the mean position error 0.074 m and RMSE 0.081 m with 97.5% map coverage; physical multi-robot tests with 5–8 robots confirm map convergence times of 120–210 steps with collision rates below 0.042 per robot per step. To the best of our knowledge, no prior physical swarm platform has simultaneously demonstrated this combination of capabilities under comparable constraints. Full article

Despite the global movement toward sustainable mobility, the adoption of electric and autonomous vehicles (EVs/AVs) in Gulf Cooperation Council (GCC) countries is shaped by unique socio-cultural and structural contingencies. This study provides a significant theoretical contribution by exploring market entry strategies through a multidimensional value framework that captures symbolic and contextual dimensions overlooked by traditional models such as TAM and UTAUT. Drawing on in-depth interviews, focus groups, and participant observations, the research utilizes Kuwait as a case study to delineate the multidimensional construct of perceived value through Osterwalder’s Value Proposition Canvas (VPC). The findings reveal that consumer adoption is influenced not only by utility and efficiency but also by social, emotional, epistemic, conditional, and cost values. Dealers, in turn, demonstrate how these values guide entry strategies for non-conventional vehicles by aligning product offerings with specific “Pain relievers”, “Gain creators”, and “Jobs to be done” (JTBD). The study identifies distinct encroachment pathways: high-end entry for battery electric vehicles (BEVs) and low-end entry for hybrid electric vehicles (HEVs). Notably, a dual-encroachment strategy is identified for high-tech Chinese brands, which are aggressively disrupting emerging markets by leveraging manufacturing efficiencies to dominate the mid-market while simultaneously deploying premium models to challenge luxury incumbents. Finally, despite the structural constraints on public AV deployment, the research highlights vital applications for autonomous systems within “industrial sandboxes” such as aviation, seaports, military, and oil sectors. While centered on Kuwait, the findings offer potentially transferable strategic insights for the broader GCC region. Full article

The extensive utilization of LiFePO₄ (LFP) batteries in energy storage facilities has been impeded by the inherent property of thermal runaway (TR). This study examines the TR propagation characteristics of 280 Ah LFP batteries and their module through the application of dual-side heating to trigger TR. Experimental investigations on single battery TR reveal that the timing and temperature at which the battery safety valve opens exhibit stochastic behavior. Moreover, a correlation is observed between the time required for the safety valve to open and the average surface temperature of the battery, with longer durations corresponding to higher temperatures. Surface temperature variations in batteries manifest in three primary phenomena: temperature decline, abrupt temperature spikes, and peak temperatures. In TR experiments involving packs, it is depicted that temperature signals can detect internal development processes earlier than smoke signals when TR initiates within the module. Heat transfer within batteries of the same sub-module primarily occurs through conduction, exhibiting an average heat transfer fraction of 25.8%. These findings hold significant implications for enhancing early detection systems for TR in both batteries and modules. Full article

This study investigates the coordinated impact of a synchronous condenser (SC), battery energy storage system (BESS), and static synchronous compensator (STATCOM) on enhancing voltage and frequency stability in a modified IEEE 9-bus power system under severe disturbances. The aim is to quantify the individual and combined contributions of these technologies during both fault ride-through (FRT) and load-increment events. The methodology includes dynamic modelling of all three devices in DIgSILENT PowerFactory. The SC is represented as a synchronous machine with inertia and AVR-based voltage control; the BESS employs converter-based active power and frequency-droop control; and the STATCOM provides fast reactive power injection through a dual-loop voltage regulator. Key indicators include nadir (minimum frequency), Rate of Change of Frequency (RoCoF), steady-state deviation, voltage sag depth, and recovery characteristics. Results indicate distinct roles for each device. The SC increases inertia and improves damping, but it also introduces small, well-damped oscillations. The BESS significantly enhances frequency stability by mitigating nadir, reducing RoCoF, and accelerating recovery, with negligible effect on voltage regulation. The STATCOM substantially reduces voltage sag and speeds up voltage recovery, but it does not influence frequency behaviour. When combined, the hybrid SC–BESS–STATCOM system demonstrates strong complementarity: the SC supports inertia, the BESS stabilizes active-power imbalance, and the STATCOM ensures fast reactive-power compensation. Full article

Accurate state of charge (SOC) estimation is essential for reliable battery management systems operating over a wide temperature range. This study proposes a joint estimation framework that combines a temperature-matched dual open-circuit-voltage (OCV)-SOC model, online forgetting-factor recursive least squares (FFRLS), and an adaptive improved multi-innovation unscented Kalman filter (AIMIUKF). The dual OCV-SOC model separately calibrates charging and discharging branches at 0 °C, 25 °C, and 45 °C, reducing the voltage bias caused by thermal dependence and charge–discharge hysteresis. On this corrected voltage baseline, FFRLS identifies the time-varying parameters of the second-order RC equivalent circuit model. The updated parameters are then used by AIMIUKF, where a finite multi-innovation window improves convergence under initial SOC deviation, and covariance matching adjusts process and measurement noise online. Validation on the CALCE 18650 dataset under the Dynamic Stress Test (DST) profile shows sub-1% SOC errors at all tested temperatures. Full article

This paper presents the design and validation of an adaptive islanding detection method (AIDM) for an AC/DC hybrid microgrid integrated with a hybrid energy storage system (HESS) comprising a supercapacitor and a battery. The proposed AIDM combines dual-tree complex wavelet transform (DTCWT), synthetic minority oversampling technique (SMOTE), and long short-term memory (LSTM) network to effectively detect islanding and non-islanding conditions in the microgrid following faults/disturbances. Fault and disturbance signals are captured at the point of common coupling, following which they are extracted and decomposed using DTCWT. The SMOTE algorithm is employed for data preprocessing to balance the dataset and enhance the accuracy of the intelligent classifier. Finally, LSTM is used for training and testing the AIDM for different faults/disturbance classification and detection. Two categories of datasets, $T_{D1}$ and $T_{D2}$, are used for testing the AIDM. The results obtained from MATLAB/Simulink show that datasets incorporated with HESS achieve higher detection accuracy of 100% compared to datasets without HESS with average accuracy of 99.77% under sudden load increase. It is also established that the proposed AIDM maintains robustness when exposed to noise signals, confirming its reliability under noisy conditions. Full article

Two-speed transmissions can regulate the motor operating point by changing the transmission ratio of drive systems and are an effective approach to improving both dynamic performance and energy efficiency of battery electric vehicles. However, existing gear shift strategies rarely consider the impact of transmission operating states on shift rationality and system stability, leading to limited adaptability under complex driving conditions. To address this issue, a hierarchical fuzzy evaluation and gear shift strategy matching method based on transmission operating states is proposed. First, three basic strategies are designed. Then, shift frequency and gear duty ratio are introduced to characterize transmission behavior, and a hierarchical decision framework consisting of driving demand evaluation, transmission behavior evaluation, and strategy matching is constructed to enable adaptive selection among different strategies. Furthermore, a fuzzy shift frequency correction strategy is proposed to adjust shift thresholds online, thereby reducing frequent and unnecessary shifting. Finally, simulations are conducted under multiple typical driving cycles based on a vehicle model, and experimental validation is carried out using a high-speed dual motor load test bench. The results demonstrate that the proposed strategy can effectively balance dynamic performance and energy efficiency while reducing unnecessary shifts. Full article

Under China’s dual-carbon agenda, a central question is whether the Low-Carbon City (LCC) pilot policy reduces energy intensity, whether this effect can be credibly interpreted as causal, and under which conditions and through which channels it operates. Using a balanced panel of 282 prefecture-level and higher-level cities from 2006 to 2023, this study develops a problem-oriented framework that integrates effect identification, credibility validation, and heterogeneity and mechanism analysis. The average treatment effect is estimated using staggered difference-in-differences, while dynamic effects are identified with interaction-weighted and imputation-based event-study estimators, and selection concerns are further addressed through propensity score matching difference-in-differences and a battery of stability checks. The results show that the LCC pilot policy reduces urban energy intensity, with the baseline estimate implying a decline of about 15–16%, and that the policy effect accumulates over time rather than appearing immediately. This finding remains stable across alternative specifications, placebo tests, and matched-sample estimation. The policy effect is stronger in cities with higher initial energy intensity and higher levels of economic development. Mechanistic evidence indicates that adjustments in fixed asset investment and changes in AI-related resource allocation are two observable channels associated with the decline in energy intensity. By focusing on energy intensity as a process-oriented performance indicator, this study provides more direct evidence on the energy-efficiency consequences of low-carbon urban governance and clarifies the conditional and structural foundations of policy effectiveness. Full article

Ultra-fast electric vehicle (EV) charging systems are among the most demanding converter-dominated applications due to their high power levels, wide battery-voltage range, strict thermal constraints, and the need for adaptive charging control. Conventional design and tuning approaches often rely on fixed control policies and computationally expensive iterative optimization, which limits their ability to address nonlinear multi-objective trade-offs across the full charging envelope. This paper proposes a hybrid AI–quantum co-design framework for a SiC-based dual active bridge (DAB) converter intended for ultra-fast EV charging applications. The proposed approach combines a physical converter model, an AI surrogate-learning layer for rapid prediction of converter performance, and a quantum-assisted optimization layer for multi-objective exploration of design and control variables. To demonstrate the framework, a representative modular 350 kW ultra-fast charging case study is considered, implemented by four parallel 87.5 kW SiC-based DAB modules and including converter-level optimization and adaptive charging-policy refinement. The revised manuscript introduces a complete system schematic, an explicit DAB converter topology, a clarified methodological workflow, and a simulation-based proof-of-concept evaluation. Representative results indicate improved design-space exploration and more balanced trade-offs between efficiency, thermal stress, ripple, and dynamic response compared with a conventional baseline tuning approach. Although the study does not claim hardware-level quantum advantage, it provides a structured and practically interpretable computational framework for intelligent co-design of high-power charging converters. Full article