Search Results (197)

Cabin temperature prediction is a key technique for improving occupant thermal comfort and reducing energy consumption of thermal management systems. Most existing cabin temperature prediction models do not integrate external meteorological information, resulting in poor adaptability to complex and variable environmental conditions. To address this issue, this paper proposes a multi-source data fusion prediction framework combined with regional meteorological information to achieve accurate cabin temperature prediction under real-world operating scenarios. Shapley additive explanations (SHAP) reveal key feature contributions and interaction mechanisms for dynamic cabin temperature prediction and improve the interpretability of the data-driven model. The precise prediction results quantify the changing patterns of cabin temperature rise and passive cooling. Based on these findings, adaptive strategies for low-temperature preheating and shutting down parking air conditioners early are designed to balance energy efficiency and occupant thermal comfort. Experimental results show that under snowy conditions, compared with the baseline model, the proposed framework reduces mean absolute errors (MAEs) by 13.05%, while the MAE decreases by 22.8% under sunny conditions. The introduction of meteorological data reduces the MAE of the XGBoost and GRU models by 14.9% and 23.28%, respectively. SHAP analysis further uncovered the feature interaction rules governing cabin temperature evolution. In addition, the proposed air-conditioning optimization strategy achieves significant energy savings without compromising occupant thermal comfort. Specifically, the steady-state energy consumption during sunny-morning preheating is reduced by 21.5%, and the pre-shutoff optimization yields energy-saving benefits ranging from 4.9% to 25.5% under typical winter conditions. This study provides theoretical support and serves as an engineering reference for the optimal design of intelligent vehicle thermal management systems. Full article

The dynamic distribution of braking forces between front and rear axles in electric commercial vehicles represents a critical multi-objective optimization challenge requiring simultaneous satisfaction of regulatory safety compliance, regenerative energy recovery, thermal stability, and actuator coordination under varying load and road conditions. This paper addresses this challenge through the development and experimental validation of an integrated adaptive brake force distribution strategy combining model predictive control (MPC) with Particle Swarm Optimization (PSO) within a unified framework that ensures compliance with ECE Regulation No. 13. A comprehensive experimental test bench was designed and instrumented, integrating three independent braking mechanisms: magnetic brakes with front and rear torque coefficients of 4.73 N·m/A and 3.65 N·m/A, respectively; an eddy current retarder with coefficient $k_0=$ 2.220 × 10⁻⁴ N·m·s/(A²·rad), producing braking torque that is quadratic in excitation current and linear in rotor speed; a regenerative braking system with 82–90% efficiency; and a switchable magnetic clutch for FWD/4WD operation. The MPC controller was formulated with a prediction horizon $N_p$ = 20, control horizon $N_c$ = 5, and sampling time $T_s$ = 20 ms. PSO was employed for systematic tuning of MPC weights using 30 particles over 50 iterations with cognitive and social coefficients $c_1$ = $c_2$ = 2.0 and linearly decreasing inertia from 0.8 to 0.4. A vehicle state estimation module using Kalman Filtering was developed for real-time estimation of vehicle mass (<3% error), road slope (<0.3% error), and road friction coefficient (<5% error). Experimental validation across eight comprehensive test scenarios demonstrates that the PSO-optimized MPC controller achieves 43% reduction in front RMSE (from 2.65 Nm to 1.52 Nm), 44% reduction in rear RMSE (from 0.78 Nm to 0.44 Nm), 100% ECE R13 compliance (improved from 67.5%), 57% settling time improvement (from 4.2 s to 1.8 s), 92% overshoot reduction (from 67% to 5%), and average recovered energy improvement from 3.51 kJ to 4.04 kJ. The proposed framework provides a comprehensive solution for next-generation electric commercial vehicle brake management systems. Full article

The coordinated operation of interconnected multi-carrier energy hubs is a key enabler of cost-efficient and flexible energy management in modern smart cities. This paper develops a comprehensive optimization framework for the day-ahead scheduling of interconnected energy hubs in residential and commercial sectors. The problem is formulated as a mixed-integer linear programming (MILP) model that jointly manages electricity, natural gas, and thermal energy flows. To enhance operational flexibility, the proposed model incorporates demand response programs for both electrical and thermal loads, multiple energy storage technologies, and electric vehicles with vehicle-to-grid (V2G) capability. Six operating scenarios are defined to assess the impact of different resources and coordination levels, ranging from independent hub operation to fully integrated interconnected scheduling. Simulation results show that coordinated operation of the energy hubs, supported by flexible loads, storage systems, and electric vehicles, can significantly reduce total daily operating costs compared with conventional standalone configurations. The findings confirm that energy exchange among hubs, combined with demand-side flexibility and EV participation, improves both economic performance and system efficiency. The proposed framework offers a scalable scheduling approach for future integrated multi-energy systems. Full article

This paper presents the development of a multi-domain simulation for the Porsche Taycan’s all-wheel-drive (AWD) electric propulsion system to investigate the impact of the rear drive unit’s two-speed transmission on performance and drive quality during maximum acceleration. This study was undertaken independent of the vehicle and propulsion system OEM. A lumped-parameter model of the front and rear electric drive units (EDU) and the high-voltage battery was developed and calibrated against the published data for key benchmarks, including 0–100 kph acceleration times and peak longitudinal acceleration. The mechanical shifting mechanism was reverse-engineered to simulate high-performance shift trajectories. To manage the transition, a clutch control scheme integrates a reduced-order clutch-to-clutch model featuring a feedforward (FF) torque estimator and a closed-loop feedback (FB) controller to achieve target input shaft speeds and shift durations. The study concludes with a comprehensive analysis of the propulsion system’s behavior at a battery state of charge of 96% and 25% and three electric motor speeds at which the upshift is commanded. The simulation results demonstrate that executing an early upshift at 10,700 rpm with 96% of SOC yields a 0.100-s inertia phase shift time, restricts the clutch thermal dissipation to 21 kJ, and achieves an 8-s velocity of 203.4 kph, outperforming the upshift at 15,300 rpm (0.210 s, 34 kJ, and 202.8 kph). Furthermore, the transient regenerative braking on the rear axle during the inertia phase reduces the peak current draw from 675 A to 87 A, recovering the DC bus voltage to enable cross-axle torque boosting on the front axle. Full article

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has emerged as a promising tool for monitoring crop physiological status and supporting precision disease management. However, the capacity of multispectral vegetation indices to detect foliar diseases under commercial field conditions remains insufficiently understood, particularly in perennial crops grown in humid tropical environments. This study evaluated the potential of UAV-derived multispectral vegetation indices to assess the physiological response of avocado (Persea americana Mill.) canopies affected by anthracnose caused by Colletotrichum fructicola in Amazonas, Peru. Disease incidence and severity were assessed through field evaluations, while multispectral imagery was acquired using a UAV equipped with a MicaSense RedEdge-MX Dual sensor. Vegetation indices including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge Index (NDRE), Red Edge Chlorophyll Index (CIred-edge), Plant Senescence Reflectance Index (PSRI), and Visible Atmospherically Resistant Index (VARI) were calculated, and their relationships with anthracnose incidence were analyzed using Spearman’s rank correlation. Field observations confirmed a high incidence of foliar anthracnose with a heterogeneous spatial distribution across the orchard. Multispectral imagery successfully characterized spatial variability in the canopy physiological condition, revealing differences in vegetation vigor, chlorophyll-related reflectance, and senescence among experimental blocks. Nevertheless, none of the evaluated vegetation indices showed statistically significant correlations with anthracnose incidence (p > 0.05), indicating that spectral variability primarily reflected the general canopy physiological status rather than a disease-specific spectral response. These findings demonstrate that UAV-derived multispectral vegetation indices are valuable for monitoring spatial variability in the avocado canopy condition but have limited capability for independently detecting anthracnose under humid tropical field conditions. Future studies integrating multi-temporal UAV acquisitions, hyperspectral and thermal imagery, LiDAR, environmental variables, and machine-learning approaches are expected to improve the early detection and spatial prediction of anthracnose in avocado production systems. Full article

Under high-Mach-number flight conditions, the combustion chambers of hypersonic vehicles encounter extreme thermal environments marked by unilateral heating, high-heat-flux density, and supercritical pressure. Traditional hydrocarbon fuel cooling often suffers from insufficient heat sinks, high-temperature cracking and coking blockages, making it difficult to meet long-endurance thermal protection requirements. Liquid metal, due to its extremely high thermal conductivity, wide liquid phase temperature range, low Prandtl number and electromagnetic pump driving capability, has become a key technology for breaking through the bottleneck of high-heat-flux thermal protection. Apart from the magnitude of heat flux, the heat-transfer time scale (such as the characteristic thermal response time of the wall and the fluid) is also crucial. During hypersonic flight, transient thermal loads can change within milliseconds, requiring rapid thermal response. Liquid metals, due to their high thermal diffusivity, have a shorter thermal diffusion time compared to hydrocarbon fuels. This review employs a systematic literature review of approaches using gallium-indium-tin alloy, GaInSn, focusing on three core directions: the flow and heat-transfer characteristics of liquid metals, the optimization of cooling micro-channels, and the application of thermal protection systems. It summarizes the research progress at home and abroad, compares and analyzes the performance differences and applicable scenarios of typical liquid-metal working fluids, and summarizes the advantages and disadvantages of existing models, structural designs, and system schemes. The research shows that liquid metals can significantly alleviate thermal stratification and eliminate coking, and deep, narrow, tree-shaped, and biomimetic micro-channels can effectively enhance heat transfer. The liquid-metal-fuel dual-channel waste heat recovery and thermoelectric power generation system has demonstrated engineering application potential. Currently, the field still faces key challenges, such as unclear heat-transfer mechanisms under extreme conditions, the lack of general heat-transfer correlation formulas, insufficient compatibility with high-temperature materials, poor miniaturization and vibration resistance of electromagnetic pumps, and low system integration. In the future, efforts should be focused on developing multi-field coupled heat-transfer models under extreme thermal environments using engineered micro-channel structures, corrosion-resistant materials, and lightweight electromagnetic pumps, promoting the research and development of integrated thermal protection, heating and power generation systems, and providing support for the development of advanced thermal management systems for hypersonic aircraft and aviation engines. Full article

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with nitrogen losses. However, the high spatial and temporal variability of soil nitrogen dynamics and crop nitrogen status makes field-scale monitoring challenging, while conventional soil and plant sampling methods are labor-intensive, destructive, and provide limited spatial coverage. Recent advances in remote sensing technologies and machine learning (ML) offer promising alternatives for high-throughput, non-destructive monitoring of crop nitrogen status and related nitrogen dynamics in agroecosystems. This review synthesizes current progress in the use of proximal and remote sensing platforms, including unmanned aerial vehicles (UAVs), satellites, and ground-based sensors for assessing crop nitrogen status and inferring soil nitrogen availability. We examine spectral, thermal, and structural indicators, together with emerging sensor-fusion and time-series approaches. We also evaluate ML algorithms, including emerging foundation model approaches, for estimating crop nitrogen status and inferring soil nitrogen indicators, highlighting their performance, limitations, and transferability across environments. Particular emphasis is placed on field-scale applications in heterogeneous and water-limited systems, where nitrogen-water interactions critically influence crop responses. Finally, we discuss current challenges, including data scarcity, model generalization, and operational constraints, and outline future directions toward integrated, real-time decision support systems for precision nitrogen management. Overall, this review provides a comprehensive framework for leveraging remote sensing and data-driven approaches to improve nitrogen monitoring and enhance nitrogen use efficiency in diverse cropping systems. Full article

Fast charging of lithium–ion batteries is essential for accelerating a widespread use of electric vehicles; however, its adoption significantly increases battery thermal stress and the risk of thermal runaway, particularly in aged cells. This study proposes a simulation-trained digital twin (DT) framework for probabilistic assessment of thermal runaway and critical charging current estimation under fast charging conditions. A dataset is generated using an electrochemical–thermal Single Particle model, varying current rate, capacity, and internal resistance. Then, an encoder–decoder neural network architecture is developed to map and convert static operating conditions into dynamic temperature evolution, enabling efficient surrogate modeling of thermal behavior. The proposed digital twin achieved an MAE of 3.05 °C and a recall of 97.22% for thermal runaway prediction while estimating critical charging currents of approximately 1.35–1.52C. The proposed methodology provides a computationally efficient tool for risk-aware fast-charging strategies, which can be integrated into battery management systems for enhanced safety. While the current study is applied to specific single-cell chemistry and simulation-based training, the framework can be easily extended to online battery systems and operating conditions. Full article

Long-endurance unmanned aerial vehicle (UAV) missions beyond 72 consecutive flight hours face a reliability boundary at which thermal gyroscope drift drives the inertial navigation system (INS) position error into rapid nonlinear divergence at a predictable threshold t_c. This paper presents BAZ II, a simulation-validated multi-agent navigation system that extends the analytical BAZ (bifurcation-aware zonal navigation) framework. Its central idea is to treat communication quality as a planning resource and combine it with multi-agent collaboration, making the navigation cliff a manageable degradation event rather than a hard operating limit. Four contributions support this idea: a thermalhysteresis MEMS gyroscope drift model reproduces the analytical cliff in simulation and supplies its physical mechanism; a distributed collaborative simultaneous localization and mapping (SLAM) filter coupled to a stochastic continuous-time Markov chain (CTMC) interagent channel sustains GPS-denied localization within the operational accuracy budget; a 3D Gaussian process RF-aware model predictive controller (MPC) with cognitive radio frequency-hopping restores link availability under jamming, while an analytic hierarchy process (AHP)-weighted multi-objective communication cost improves latency and jitter at negligible signal-to-noise ratio cost; finally, the integrated controller executes within the onboard real-time budget of an NVIDIA Jetson Xavier NX. All results are obtained in simulation, with hardware-in-the-loop and field testing remaining as priority future work. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

In this study, a liquid-cooled Battery Thermal Management System (BTMS) incorporating aluminum heat-conducting blocks was numerically investigated to enhance the thermal performance of lithium-ion battery modules used in electric vehicles. The proposed system was designed for a battery module consisting of cylindrical lithium-ion cells, and the effects of different geometric configurations on thermal behavior were analyzed using the Computational Fluid Dynamics (CFD) method. To efficiently evaluate the multi-parameter design space with reduced computational cost, a Taguchi L9 orthogonal experimental design was employed. The cooling channel configuration, aluminum heat-conducting block height, and battery pack geometry were considered as the primary design variables. The performance of each design configuration was assessed based on maximum temperature (T_max) and temperature uniformity (ΔT). Furthermore, an Analysis of Variance (ANOVA) was conducted to quantify the influence of the design parameters on the thermal performance of the system. The results revealed that the configuration comprising eight cooling channels, a 65 mm aluminum block height, and a 1 + 8 cylindrical battery arrangement exhibited the best thermal performance, achieving a maximum temperature of 303.45 K and a temperature difference of 1.25 K. The optimal design configuration provided a more uniform temperature distribution within the battery module, thereby enhancing thermal safety and operational reliability. Overall, the integration of CFD and the Taguchi method offers a systematic and efficient optimization framework for BTMS design, enabling effective evaluation of design alternatives with a reduced number of simulations and shorter computational time. Full article

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its limited energy density restricts operational range and mission flexibility. As a result, micro gas engines have emerged as a viable alternative for applications requiring high power-to-weight ratios and sustained high-speed operation. This review examines the fundamentals, scaling effects, and classification of micro gas turbine propulsion systems used in UAV applications, with emphasis on micro turbojets and related hybrid configurations. The paper discusses the thermodynamic principles governing micro gas engines and analyzes the aerodynamic, thermal, and combustion challenges associated with miniaturization, including low Reynolds number effects, tip leakage losses, thermal management limitations, and combustion instability. Furthermore, the study reviews the operational characteristics and mission suitability of different propulsion architectures for reconnaissance UAVs, high-speed UAVs, including reconnaissance and loitering platforms, target drones, and hybrid-electric aerial platforms. Recent developments involving additive manufacturing, advanced control systems, recuperated cycles, and hybrid-electric integration are also evaluated as enabling technologies for next-generation UAV propulsion. The findings demonstrate that although micro gas turbines continue to face important efficiency and manufacturing challenges at reduced scales, they remain essential for mission profiles that exceed the capabilities of purely electric propulsion systems. Full article

Electric vehicle (EV) powertrains involve complex interactions between speed regulation, energy consumption, regenerative braking, and battery thermal behavior. Most existing approaches address controller tuning and energy management separately, which may limit the overall system performance. This paper proposes an integrated deep reinforcement learning (DRL) strategy in which a single Twin Delayed Deep Deterministic Policy Gradient (TD3) agent simultaneously adjusts the proportional and integral gains of the speed controller (K_pv, K_iv), the torque modulation coefficient (K_s), and the regenerative braking factor (β_reg). A multi-objective reward formulation is adopted to account for speed tracking performance, energy efficiency, regenerative energy recovery, battery thermal constraints, and driving comfort. The framework is implemented through a MATLAB R2022b/Simulink–Python 3.10 co-simulation environment that enables online interaction between the EV model and the learning agent. Performance is evaluated using the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). Compared with a conventional fixed-gain PI controller, the approach reduces gross energy consumption by 16.2%, decreases speed tracking error by 43.7%, increases regenerative energy recovery by 21.4%, limits battery temperature rise by 30.4%, and lowers RMS jerk by 33.7%. The results indicate that jointly optimizing control and energy management variables can improve both vehicle dynamic performance and energy utilization. The methodology offers a practical framework for the development of adaptive and intelligent control systems in future electric vehicles. Full article

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

Axial-flux permanent-magnet (AFPM) machines are becoming an increasingly promising solution for electromechanical systems requiring high power density. In particular, their use is expanding to electric vehicles (EVs), the aerospace industry, and advanced industrial applications, such as renewable energy applications. Their compact design, high torque-to-mass ratio, and relatively high efficiency make AFPM machines an attractive alternative to traditional radial-flux solutions. However, their integration for widespread application remains limited due to challenges in design, manufacturing, thermal management, and control systems, which ultimately also have an economic impact. This article presents a comprehensive and systematic review of AFPM machines, covering key aspects, including topology classification, design methodologies, electromagnetic modelling, optimisation methods, materials and manufacturing processes, and advanced control strategies. A structured, multi-level classification of AFPM machines is presented, incorporating stator and rotor configurations, magnetic circuit structures, winding types, and materials, thereby providing a unified overview of existing designs. Furthermore, the article presents an in-depth analysis of the sizing equations used to calculate and estimate the parameters, approaches to electromagnetic modelling (including the finite element method and magnetic equivalent circuits), and modern optimisation methods based on artificial intelligence. Particular attention is paid to materials science and new manufacturing technologies, such as soft magnetic composites, printed circuit board stators, and additive manufacturing, as well as to thermal management solutions required for high-power-density applications. This work provides a unified reference framework for researchers and engineers and outlines future directions for the development and industrial adoption of AFPM machines. Full article