Search Results (256)

Model Predictive Control (MPC) has emerged as a promising approach for energy management in hybrid electric vehicles, enabling predictive optimization of powertrain operation. The energy management problem in parallel hybrid powertrains constitutes a Mixed-Integer Nonlinear Programming (MINLP) problem, combining continuous decision variables such as torque distribution with discrete decisions including engine on/off states and clutch engagement. This problem structure presents distinct challenges for different optimization approaches. Gradient-based methods such as Sequential Quadratic Programming (SQP) solve continuous, differentiable optimization problems and require auxiliary methods to handle integer variables, while metaheuristic approaches such as Genetic Algorithms (GA) can handle the mixed-integer structure directly at the cost of increased computational effort. This study presents a systematic comparison between GA and SQP as optimization solvers within an MPC framework for a P1P3 parallel hybrid powertrain. A multi-objective cost function is formulated to simultaneously optimize system efficiency, battery state of charge management, and noise emissions. Both approaches are evaluated across the WLTC as well as a real-world RDE scenario. On the WLTC, both MPC approaches reduce fuel consumption by 0.5–1.0% and improve system efficiency by 3.7–4.6% compared to a state-of-the-art deterministic reference strategy optimized for fuel consumption. At the same time, both approaches additionally achieve substantial reductions in noise emissions compared to the deterministic reference, which was not optimized for acoustic behavior. On both cycles, the GA-based MPC achieves favorable performance compared to SQP, with the performance gap widening from the WLTC to the RDE cycle. Both methods achieve real-time capability, yet SQP reduces computational time by a factor of four compared to GA. As long as computational resources in automotive ECUs remain constrained, this efficiency advantage positions gradient-based optimization for series production applications, whereas metaheuristic methods offer greater flexibility for concept development stages with relaxed real-time requirements. The findings contribute to the understanding of optimization algorithm selection for MINLP energy management problems in hybrid electric vehicles. Full article

Under global climate change, frequent droughts threaten ecosystem functions, but how drought characteristics affect ecosystem resilience remains unclear. Focusing on the Dongjiang River Basin, China, we identified drought events at an 8-day scale from 2000–2024 using multi-source remote sensing and reanalysis data. The water use efficiency-based resilience index (R_de) was calculated, and a random forest model quantified the contributions of 21 potential driving factors. The model explained 68% of R_de variance (R² = 0.68, RMSE = 0.12). Downward shortwave radiation was the primary factor, followed by antecedent water use efficiency and soil moisture anomaly, with drought intensity and air temperature ranking fourth and fifth. All dominant factors exhibited nonlinear threshold effects: R_de decreased significantly after radiation exceeded ~110 W·m⁻²·(8d)⁻¹; R_de declined when standardized soil moisture anomaly fell below −2.0; and R_de increased sharply when drought intensity exceeded 12%. Drought intensity far outweighed duration and severity, establishing it as the key drought attribute. This study reveals the dominant drivers and their thresholds governing ecosystem resilience in the Dongjiang River Basin, providing quantifiable indicators for ecological drought early warning. Full article

Detonation-based combustion has re-emerged as a promising pathway for enhancing the efficiency and compactness of future aerospace propulsion systems, motivated by the intrinsic pressure-gain characteristics of detonative heat release. This review provides a comprehensive synthesis of the physical foundations, technological progress, and practical limitations associated with pulse detonation engines, rotating detonation engines, and standing or oblique detonation wave concepts. By tracing the evolution from early theoretical models and laboratory-scale demonstrations to engine-relevant configurations, this article highlights how detonation physics, ignition mechanisms, wave stability, and flow–structure interactions collectively govern propulsion performance. Particular attention is paid to recent experimental and numerical studies, with the review focusing on their technological impact and on the feasibility of integrating detonation-based propulsion concepts into practical aerospace systems. The analysis evaluates these approaches’ potential to enhance system-level performance compared to conventional propulsion technologies, while highlighting key challenges associated with scalability, operability, and compatibility with existing aerospace architectures. The review further identifies emerging design strategies, including geometry tailoring, adaptive flow control, and hybrid architectures, as key enablers for extending operability and system integration. Overall, the findings indicate that future progress in detonation-based propulsion will depend less on demonstrating detonation itself and more on achieving robust, controllable, and scalable implementations suitable for realistic aerospace applications. Full article

Unmanned aerial vehicles (UAVs) require reliable autonomous positioning independent of external satellite navigation signals, motivating the development of a vision-based, end-to-end finding point in map (FPI) framework. This study introduces MuRDE-FPN, an enhanced feature pyramid network (FPN) designed for precise UAV localization, building upon a lightweight one-stream transformer-based (OS-PCPVT) backbone. MuRDE-FPN integrates efficient channel attention (ECA) for adaptive channel recalibration and features two novel components: a multi-receptive deformable enhancement (MuRDE) that utilizes deformable convolutions with varying kernel sizes to refine the semantically rich final feature layer, and a feature alignment module (FAM) for cross-level fusion. Evaluated on the UL14 dataset and a new, more diverse UAV-Sat dataset, MuRDE-FPN consistently outperformed four state-of-the-art FPI methods (FPI, WAMF-FPI, OS-FPI, DCD-FPI). It achieved a relative distance score of 84.26 on UL14 and 63.74 on UAV-Sat datasets, demonstrating improved localization. Ablation studies confirmed the cumulative benefits of ECA, MuRDE, and FAM. These findings highlight the effectiveness of custom FPN designs and targeted feature enhancements for precise cross-view positioning, with MuRDE-FPN providing a robust solution and the UAV-Sat dataset offering a new benchmark for evaluation. Future efforts will address computational efficiency and performance across varying data quality environments. Full article

This study quantifies the effect of air and fuel filter restriction on fuel consumption, regulated pollutants (CO and HC), and CO₂ greenhouse gas emissions under real driving conditions in a hilly high-altitude environment. Four filter configurations were evaluated: clean air filter–clean fuel filter (CAF–CFF, reference), dirty air filter–clean fuel filter (DAF–CFF), clean air filter–dirty fuel filter (CAF–DFF), and dirty air filter–dirty fuel filter (DAF–DFF). Each test was repeated three times over the same RDE route in Quito (≈2100–2900 m). Fuel consumption was estimated from ECU-based signals, and CO₂ emission factors and regulated pollutant (CO and HC) emission factors were computed from measured exhaust concentrations and distance normalization. Results were analyzed by RDE section (urban, rural, motorway) and expressed as percent changes relative to the reference configuration to directly isolate filter restriction effects. Relative to CAF–CFF, DAF–CFF produced the largest increase in average fuel consumption (+7.2%) and the largest urban CO₂ penalty (+22.7%), indicating a strong efficiency sensitivity to intake restriction under transient operation. CAF–DFF increased average fuel consumption by 6% and produced the strongest motorway penalties for CO (+77.3%) and HC (+44.4%), suggesting that fuel delivery restriction has a stronger influence on incomplete oxidation products under sustained higher load. The combined restriction (DAF–DFF) showed non-additive responses depending on the operating regime. Random Forest models were trained to estimate CO₂, CO, and HC, achieving R² values of 0.8571, 0.8229, and 0.7690, respectively, while multiple linear regression achieved an R² of 0.852 for fuel consumption. The proposed approach supports data-driven monitoring of filter restriction effects under real driving operation, while acknowledging that fuel consumption and CO₂ are obtained through different measurement and conversion paths and may not yield identical percent changes. Full article

The complexity of modern vehicle control systems, the increasing diversity of powertrain and exhaust aftertreatment applications, and the need for shortened development times require innovative approaches towards calibration. This paper presents an experimental, analytical, and modeling study of particulate filter (commonly called DPF—diesel particulate filter) in a diesel hybrid vehicle where models have been developed to simulate test data, replacing the requirement of numerous tests on testbed or on the road with system simulations and offline parameter optimisation techniques. A soot estimation model has been developed based on the operation of the engine including its transient response, and the thermal–chemical behaviour of the DPF. A methodology has been developed to optimize the calibratable maps and parameters within this model. The results show that the proposed method improves the accuracy of soot estimation in the engine transient operation and avoids a large number of experimental tests required in traditional calibration methods. Modern automotive manufacturers face regulatory compliance requirements ensuring emission standards across diverse real driving emission (RDE) boundary conditions encompassing route characteristics, driving dynamics, and ambient environmental variables throughout vehicles’ operational lifetime. The soot load in the DPF and the DPF regeneration frequency can massively impact the tailpipe NO_x emissions and overall fuel consumption, so it is key to accurately estimate the soot accumulation in all operating conditions. This means testing and validating calibration in each possible scenario and so needs an enormous number of tests on testbed and on the road. These tests, however, can be replaced with system simulations and offline calibration if we have a robust model for the system, as described in the following parts of this paper. Full article

In this paper, several optimized design results of the HTGR-based 10 MWth Reaktor Daya Eksperimental (RDE) (Experimental Power Reactor), so far conducted, are reviewed and compared from the neutronics, reactor types, refueling schemes, and fuel cycle points of view. The review covers the multipass and once-through-then-out (OTTO) pebble-bed cores, as well as block/prismatic type cores with several fuel shuffling options. As for the fuel cycle, uranium and thorium fuels are considered. The fuel burnup performance and power distribution are evaluated and compared among other important design parameters. Reactor physics codes, nuclear data libraries, and calculation models and procedures used for the design and analysis are reviewed, and challenges for future improvements are discussed. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Decarbonisation of the transport sector, whilst reducing pollutant emissions, will likely involve the utilisation of multiple strategies, including hybridisation and the use of alternative fuels such as advanced biofuels as mandated by the EU. Alcoholysis of lignocellulosic feedstocks, using n-butanol as the solvent, can produce such potential advanced biofuel blends. Butyl blends, consisting of n-butyl levulinate (nBL), di-n-butyl ether, and n-butanol, were selected for this study. Three butyl blends with diesel, two at 10 vol% biofuel and one at 25 vol% biofuel, were tested in a Euro 6b-compliant diesel hybrid vehicle to determine the influence of the blends on regulated emissions and fuel economy. Real Driving Emissions (RDE) were measured for three cold start tests with each fuel using a Portable Emissions Measurement System (PEMS) for carbon monoxide (CO), particle number (PN), and nitrogen oxides (NO_X = NO + NO₂). When using the butyl blends, there was no noticeable change in vehicle drivability and only a small fuel economy penalty of up to 5% with the biofuel blends relative to diesel. CO, NO_X, and PN emissions were below or within one standard deviation of the Euro 6 not-to-exceed limits for all fuels tested. The CO and PN emissions reduced relative to diesel by up to 72% and 57%, respectively. NO_X emissions increased relative to diesel by up to 25% and increased with both biofuel fraction and the amount of nBL in that fraction. The CO emitted during the cold start period was reduced by up to 52% for the 10 vol% blends but increased by 25% when using the 25 vol% blend. NO_X and PN cold start emissions reduced relative to diesel for all three biofuel blends by up to 29% and 88%, respectively. It is envisaged that the butyl blends could reduce net carbon emissions without compromising or even improving air pollutant emissions, although optimisation of the after-treatment systems may be necessary to ensure emissions limits are met. Full article

Test fires of a rotating detonation engine (RDE) annular combustor operating on a methane–oxygen mixture were conducted. Compared to the original RDE combustor previously tested, it was modified in terms of changing the layout of the water cooling system, the positions of ports for sensors, and the shape of the supersonic nozzle. The stable operation process with a single detonation wave continuously rotating in the annular gap with the velocity of ~1900 m/s (rotation frequency of ~6 kHz) was obtained in the wide range of flow rates of propellant components. This is an important distinguishing feature of the present RDE combustor compared to the analogs known from the literature, which usually exhibit an increase in the number of simultaneously rotating detonation waves with an increase in the flow rates of propellant components. Compared to the original RDE combustor, the maximum duration of operation and the attained sea-level specific impulse were increased from 1 to 30 s and from 250 to 277 s, respectively. The thermal states of all heat-stressed elements of the combustor were obtained. The maximum heat fluxes are registered in the water cooling jackets of the central body and the combustor outer wall. Heat losses in the water cooling system are shown to increase with the average pressure in the combustor. The maximum value of the average heat flux over 20 MW/m² is achieved on the combustor outer wall. The average heat flux into the combustor outer wall is approximately 20% higher than that into the central body. The average heat flux into the nozzle is several times lower than similar values for the combustor outer wall and central body. The total heat loss into the water-cooled walls of the combustor reach about 10% of the total thermal power of the combustor. Full article

The combustion diagnostics of rotating detonation engines (RDE) based on excited-state hydroxyl radical (OH*) chemiluminescence imaging is an important method used to characterize combustion flow fields. Overcoming the limitations of imaging devices to achieve nanosecond-scale temporal resolution is crucial for observing the propagation of high-frequency detonation waves. In this work, a nanosecond-scale imaging system with an ultra-high spatiotemporal resolution was designed and constructed. The system employs four near ultraviolet (NUV)-visible ICMOS, equipped with a high-gain, dual-microchannel plate (MCP) architecture fabricated using a new atomic layer deposition (ALD) process. The system has a maximum electronic gain of 10⁷, a minimum integration time of 3 ns, a minimum interval time 4 ns, and an imaging resolution of 1608 × 1104 pixels. Using this system, high-spatiotemporal-resolution visualization experiments were conducted on RDE, fueled by H₂–oxygen-enriched air and NH₃–H₂–oxygen-enriched air. The results enable the observation of the detonation wave structure, the cellular structure, and the propagation velocity. In combination with optical flow analysis, the images reveal vortex structures embedded within the cellular structure. For NH₃-H₂ mixed fuel, the results indicate that detonation wave propagation is more unstable than in H₂ combustion, with a larger bright gray area covering both the detonation wave and the product region. The experimental results demonstrate that high spatiotemporal OH* imaging enables non-contact, full-field measurements, providing valuable data for elucidating RDE combustion mechanisms, guiding model design, and supporting NH₃ combustion applications. Full article

The reproducibility of oxygen reduction reaction (ORR) activity assessment for platinum-based electrocatalysts using the rotating disk electrode (RDE) method is critically dependent on the quality of the fabricated catalytic layer. This work presents a comprehensive study on optimizing catalytic ink formulation—specifically the water-to-isopropanol (H₂O:IPA) solvent ratio and the ionomer-to-carbon (I/C) ratio—to achieve a homogeneous catalytic layer and ensure high data reproducibility for monometallic Pt/C and bimetallic PtCu/C catalysts. A key aspect of this research is the implementation of a simple and effective visual inspection method using a benchtop digital microscope to rapidly assess catalytic layer quality, which was shown to correlate directly with electrochemical performance. The optimal ink composition was found to be catalyst-specific. For Pt/C, the highest mass activity of 353 A/g~Pt~ was achieved with a solvent ratio of 1:3 (H₂O:IPA) and an I/C ratio of 0.3. For PtCu/C, the best performance was obtained with the same solvent ratio (1:3) but a higher I/C ratio of 0.4, yielding a mass activity of 491 A/g~Pt~. It was demonstrated that ink compositions leading to layer inhomogeneities, such as aggregates and “coffee-ring” effects, significantly impair mass transport and lead to underestimated ORR activity. The study underscores the absence of a universal ink recipe and establishes that the optimization of ink parameters for each specific catalyst is essential for obtaining reliable and reproducible electrochemical data. Full article

Electric vehicles have a limited driving range compared to conventional vehicles. This paper aims to present a particular study of the performances of electric vehicles based on real driving conditions on a cycle carried out using the general conditions of European Regulation regarding RDE tests. In this sense, a modern electric SUV was used and experimental tests were conducted using a measuring equipment connected to the vehicle via the OBD2 plug. The experimental results will be analyzed and presented in the following sections, and the main influencing factors on the energy performances (range, state of charge, energy consumption) that may occur in real running conditions will be identified. Full article

With rural revitalization and industrial upgrading, a single electrical perspective can no longer meet diversified energy demands. Meanwhile, rapid growth of distributed resources such as photovoltaics, storage, and biomass enables multi-energy complementarity. This paper proposes a hierarchical distributed optimization framework for Rural Distributed Energy Systems (RDES) explicitly considering storage aggregation. First, basic models are developed for diverse resources in the RDES, and Minkowski sum and inner approximation methods are used for storage aggregation. Considering electricity, heat, and cooling, a two-level operation model is built at both the distribution network and transformer area levels. Then, an ADMM-based distributed algorithm coordinates multiple rural energy areas and the distribution network through iterative interactions. Finally, an integrated energy test network based on the IEEE-30 system verifies that the model minimizes overall operational cost while protecting district interests and ensuring thermal–electrical network safety. Full article

Reproducing severe emission driving scenarios on a chassis dynamometer enables the systematic calibration of real driving emissions (RDE) under laboratory conditions. Accordingly, a dynamic programming (DP) method is proposed to construct emission-intensive driving cycles for an extended-range electric vehicle. The DP approach transforms the driving cycle construction problem into one of multi-stage decision optimization within a time control domain. Assembling a real driving emission model and a multi-stage decision optimization model, a DP algorithm was developed. Guided by real-world trip dynamics and road terrain, the DP algorithm optimizes instantaneous vehicle driving conditions at every time step, thereby reconstructing vehicle speed and road gradient profiles to maximize pollutant emissions within the time control domain. Analysis demonstrates that the DP algorithm favors constructing emission-intensive driving cycles using high-frequency, low-intensity acceleration and deceleration maneuvers, in addition to the high-aggression driving typically assumed to cause the severest emissions. Furthermore, the DP algorithm also effectively utilizes the impact of road terrain on emissions to construct these driving cycles. Verification confirms that the constructed emission-intensive driving cycles not only exhibit severe emission characteristics but also conform to the mandatory RDE test requirements in trip dynamics and road terrain conditions. Full article