Search Results (102)

The transition to electric vehicles (EVs) plays a critical role in reducing global carbon emissions. However, the end-of-life management of electric vehicle batteries (EVBs) presents significant sustainability and operational challenges. This study proposes a blockchain-based framework that enables full lifecycle tracking of EVBs, from production to disposal or reuse, while addressing issues of transparency, efficiency, and regulatory compliance. The framework incorporates a multi-criteria decision model to guide data-driven end-of-life routing—whether for second-life reuse or direct recycling—based on technical, environmental, and economic indicators. By integrating smart contracts with a hybrid web/mobile platform, the system ensures tamper-proof documentation, stakeholder accountability, and compliance with the EU battery passport regulation. A detailed cost analysis of deploying the framework on Ethereum is also presented. The proposed solution aims to enhance the sustainability of EVB management, reduce environmental impact, and promote circular economy practices within the EV industry. Full article

This paper presents a detailed experimental evaluation of a newly developed diesel fuel additive, specifically formulated to enhance the energy efficiency and emission characteristics of internal combustion engine (ICE) vehicles, with particular emphasis on its applicability to aging vehicle fleets. Diesel engines are known for producing significant amounts of harmful emissions, necessitating the development of effective mitigation strategies. One such approach involves the use of fuel additives. The additive under investigation is a proprietary formulation containing 1-(N,N-bis(2-ethylhexyl)aminomethyl)-1,2,4-triazole and other compounds. To the best of our knowledge, this specific additive composition has not yet been tested or reported in the existing scientific literature. To evaluate the real-world contribution of such additives, a comprehensive set of controlled measurements was conducted in a certified chassis dynamometer laboratory, including an exhaust gas analyser and supplementary diagnostic equipment. The testing protocol comprised repeated measurement cycles under identical driving conditions, both without and with the additive. Exhaust gas concentrations of CO₂, CO, and NOx were continuously monitored. Simultaneously, fuel consumption and engine performance were tracked over a cumulative driving distance of 2000 km. The results indicate measurable improvements across all monitored domains. CO₂ emissions decreased by 4.57%, CO by 14.29%, and NOx by 3.12%. Fuel consumption was reduced by 4.79%, while engine responsiveness and power delivery showed moderate but consistent enhancements. These improvements are attributed to more complete combustion and an increased cetane number enabled by the additive’s chemical structure. The findings support the adoption of advanced additive technologies as part of transitional strategies towards low-emission transportation systems. Full article

This paper proposes a terminal area energy management (TAEM) guidance system using an online ground track predictor (GTP) for an uncrewed space vehicle (USV). Based on the current geometric range method for each separate phase, we establish a real-time range-to-go calculation method for generating reference commands online. The method ensures continuous range-to-go variation through status flags and an integrated range, thereby avoiding sudden command changes at subphase transitions, which may reduce longitudinal tracking stability. To enhance adaptability in an initial low-energy state, the system tracks the low-energy reference trajectory to provide an additional lift-to-drag margin, thus preventing an overly low terminal velocity. The results of numerical simulations with multiple uncertainties validate the proposed guidance strategy. Moreover, the flight test results confirm its ability to direct the USV to the target position with the desired energy state in real-world conditions. Full article

Aiming at the problem of model parameter perturbation in vehicle trajectory tracking control, an improved robust model predictive control (RMPC) method is proposed. Based on the two-degree-of-freedom vehicle model and Serret Frenet error model, a multi-cell hypercube vertex modeling is adopted to map the disturbance range of parameters such as vehicle speed and lateral stiffness to a set of vertices, and dynamic linear combination is achieved through normalized weights. The algorithm design mainly focuses on the dual-layer optimization of the switching mechanism, decomposing the infinite time domain problem into finite time domain optimization and terminal constraints. At the same time, it dynamically updates the vertex parameters to match time-varying uncertainties and then combines Lyapunov theory to design a control invariant set. The results show that in complex road conditions and vehicle state transitions, RMPC can reduce the peak lateral deviation from 1.0 m to 0.2 m, converge the heading deviation to within 2 deg, and significantly reduce the mean and root mean square values of control errors compared to traditional MPC, under the influence of vehicle model parameter perturbations. RMPC has good robustness and real-time performance. Full article

In complex Unmanned Aerial Vehicle (UAV) trajectory tracking scenarios, conventional Interacting Multiple Model (IMM) algorithms face challenges such as slow model switching rates and insufficient tracking accuracy. To address these limitations, this paper proposes an enhanced algorithm named Adaptive Interacting Multiple Model-Improved Adaptive Kalman Filter (AIMM-IAKF). The AIMM component dynamically adjusts the model transition probability matrix based on real-time model probability differences, overcoming the limitation of a fixed matrix in traditional IMM. Furthermore, the conventional Kalman filter is replaced with an Improved Adaptive Kalman Filter (IAKF), which introduces a convergence criterion and a suboptimal fading factor to optimize noise statistics. Simulation results demonstrate that, compared to the traditional IMM algorithm, the proposed AIMM-IAKF algorithm improves tracking accuracy by approximately 69%, achieves a faster model switching response, and exhibits superior stability with lower error fluctuation. The proposed framework provides a highly accurate and robust solution for tracking highly maneuvering UAVs. Full article

South Korea faces high levels of air pollution and is currently not on track to meet its transport sector 2030 and 2050 greenhouse gas emission reduction targets primarily due to infrastructural limitations. This study examines the potential health benefits of a more rapid green transition of South Korea’s transport sector from 2026 to 2050 in terms of avoided premature deaths and years of life lost due to reduced ambient PM_2.5 exposure. The research conducts a scenario analysis comparing the business-as-usual trajectory of the transport sector with two alternative scenarios. In the first alternative scenario, South Korea’s transport sector achieves its 2030 NDC in 2035 and carbon neutrality in 2050 with a reliance on CCUS for emission capture. The second alternative scenario entails stronger climate action in which the transport sector meets the 2030 NDC target in 2030 and the 2050 carbon neutrality transport sector target through a complete green transition to electric vehicles and hydrogen vehicles. The first alternative scenario results in an average of 80 avoided premature deaths (775 avoided years of life lost) and 53 MTCO₂e avoided emissions per year from 2026 to 2050. The second more rapid green transition scenario of South Korea’s transport sector achieves an average of 96 avoided premature deaths (925 avoided years of life lost) and 66 MTCO₂e avoided emissions per year. This research supports a more rapid green transition of South Korea’s transport sector for both health and climate gains. Full article

Unmanned Aerial Vehicles (UAVs) have revolutionized various industries due to their adaptability, efficiency, and capability to operate in diverse environments. However, conventional UAV designs face trade-offs between flight endurance and maneuverability. This study explores the design, analysis, and optimization of a biplane quadrotor UAV, integrating the vertical takeoff and landing (VTOL) capabilities of multirotors with the aerodynamic efficiency of fixed-wing aircraft to enhance flight endurance while maintaining high maneuverability. The UAV’s structural design incorporates biplane wings with different NACA airfoil configurations (NACA4415, NACA0015, and NACA0012) to assess their impact on drag reduction, stress distribution, and flight efficiency. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent 2023 R2 (Canonsburg, PA, USA).reveal that the NACA0012 airfoil achieves the highest drag reduction (75.29%), making it the most aerodynamically efficient option. Finite Element Analysis (FEA) further demonstrates that NACA4415 exhibits the lowest structural stress (95.45% reduction), ensuring greater durability and load distribution. Additionally, a hybrid flight control system, combining Backstepping Control (BSC) and Integral Terminal Sliding Mode Control (ITSMC), is implemented to optimize transition stability and trajectory tracking. The results confirm that the biplane quadrotor UAV significantly outperforms conventional quadcopters in terms of aerodynamic efficiency, structural integrity, and energy consumption, making it a promising solution for surveillance, cargo transport, and long-endurance missions. Future research will focus on material enhancements, real-world flight testing, and adaptive control strategies to further refine UAV performance in practical applications. Full article

This article presents an in-depth study of a dynamic wireless power transfer (DWPT) system used to charge electric vehicles (EVs), with a focus on modeling and controlling a double-D (DD) coil structure. The chosen DD coil design improves energy transfer efficiency and minimizes mutual coupling between adjacent transmit coils, a common problem in dynamic applications. A comprehensive mathematical model is developed to account for the nonlinear dynamics of the system, i.e., when the vehicle is moving and misalignments and coupling variations occur. A robust nonlinear control method based on sliding mode control (SMC) is implemented to ensure stable operation and accurate regulation of the output voltage. The controller is tested in different scenarios where the vehicle speed changes, thus ensuring its robustness and stability under all operating conditions. Particular attention is paid to the critical transition zone, in which the receiver coil is placed between two transmitter coils in order to achieve minimal magnetic coupling. The simulation results demonstrate that the proposed controller offers a fast dynamic response (~0.07 s) and stable voltage tracking, even in the event of significant variations in mutual inductance and different EV movement speeds. These results confirm the effectiveness of the control approach and its potential for real-time charging of electric vehicles in large-scale DWPT applications. Full article

This study proposes a current control strategy to enhance the control stability of an interior permanent magnet synchronous motor (IPMSM) under transient conditions, such as rapid acceleration or deceleration in electric vehicle (EV) applications. Conventional current control methods provide optimal steady-state current references corresponding to torque commands using a lookup table (LUT)-based approach. However, during transitions between these reference points, particularly in the field-weakening region at high speeds, the voltage limit may be exceeded. When the voltage limit is exceeded, unstable overmodulation states may occur, degrading stability and resulting in overshoot of the inverter input current. Although ramp generators are commonly employed to interpolate between current references, a fixed ramp slope may fail to ensure a sufficient voltage margin during rapid transients. In this study, a method is proposed to dynamically adjust the rate of change of the d-axis current reference in real time based on the difference between the inverter output voltage and its voltage limit. By enabling timely field-weakening before rapid changes in speed or q-axis current, the proposed strategy maintains control stability within the voltage limit. The effectiveness of the proposed method was verified through simulations based on real vehicle driving profiles and dynamometer experiments using a 38 kW class IPMSM for a hybrid electric vehicle (HEV), demonstrating reduced input DC current overshoot, improved voltage stability, and enhanced torque tracking performance under high-speed transient conditions. Full article

The increasing demand for high-performance power converters for electric vehicle (EV) applications places a significant emphasis on developing effective and robust control strategies for DC-DC converter operation. This paper deals with the development, simulation, and experimental validation of an adaptive Lyapunov-type Nonlinear Sliding Mode Control (L-SMC) strategy for a DC–DC boost converter, addressing significant uncertainties caused by large variations in system parameters (R and L) and ensuring the tracking of a voltage reference. The proposed control strategy employs the Lyapunov stability theory to build an adaptive law to update the parameters of the sliding surface so the system can achieve global asymptotic stability in the presence of uncertainty in inductance, capacitance, load resistance, and input voltage. The nonlinear sliding manifold is also considered, which contributes to a more robust and faster convergence in the controller. In addition, a logic optimization technique was implemented that minimizes switching (chattering) operations significantly, and as a result of this, increases ease of implementation. The proposed L-SMC is validated through both simulation and experimental tests under various conditions, including abrupt increases in input voltage and load disturbances. Simulation results demonstrate that, whether under nominal parameters (R = 320 Ω, L = 2.7 mH) or with parameter variations, the voltage overshoot in all cases remains below 0.5%, while the steady-state error stays under 0.4 V except during the startup, which is a transitional phase lasting a very short time. The current responds smoothly to voltage reference and parameter variations, with very insignificant chattering and overshoot. The current remains stable and constant, with a noticeable presence of a peak with each change in the reference voltage, accompanied by relatively small chattering. The simulation and experimental results demonstrate that adaptive L-SMC achieves accurate voltage regulation, a rapid transient response, and reduces chattering, and the simulation and experimental testing show that the proposed controller has a significantly lower steady-state error, which ensures precise and stable voltage regulation with time. Additionally, the system converges faster for the proposed controller at conversion and is stabilized quickly to the adaptation reference state after the drastic and dynamic change in either the input voltage or load, thus minimizing the settling time. The proposed control approach also contributes to saving energy for the application at hand, all in consideration of minimizing losses. Full article

Standoff tracking of a ground-moving target with a single fixed-wing unmanned aerial vehicle (UAV) is vulnerable to occlusions around the target, such as buildings and terrain, which can obstruct the line of sight (LOS) between the UAV and the target, resulting in tracking failures. To address these challenges, multi-UAV cooperative tracking is often employed, offering multi-angle coverage and mitigating the limitations of a single UAV by maintaining continuous target visibility, even when one UAV’s LOS is obstructed. Building on this idea, we propose a specialized two-layer framework for dual-UAV cooperative target tracking. This framework comprises a decision-making layer and a guidance layer. The decision-making layer employs a state-transition-based distributed role transition algorithm for dual UAVs. Here, the UAVs periodically share state variables based on their target observability. In the guidance layer, we devise a velocity-vector-field-based controller to simplify the complexity of controller design for cooperative tracking. To validate the proposed framework, three numerical simulations and one hardware-in-the-loop (HIL) simulation were conducted. These simulations confirmed that the role transition algorithm functions properly even under occlusion conditions. Additionally, the standoff tracking guidance controller demonstrated superior performance compared to baseline methods in terms of tracking accuracy and stability. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Robots with multiple degrees of freedom (DOFs), such as underwater vehicle–manipulator systems (UVMSs), are expected to optimize system performance by exploiting redundancy with various basic tasks while still fulfilling the primary objective. Multiple tasks for robots, which are expected to be carried out simultaneously with prescribed priorities, can be referred to as sets of tasks (SOTs). In this work, a hybrid vision/force control method with continuous task transitions is proposed for a UVMS to simultaneously track the reference vision and force trajectory during manipulation. Several tasks with expected objectives and specific priorities are established and combined as SOTs in hybrid vision/force tracking. At different stages, various SOTs are carried out with different emphases. A hierarchical optimization-based whole-body control framework is constructed to obtain the solution in a strictly hierarchical fashion. A continuous transition method is employed to mitigate oscillations during the task switching phase. Finally, comparative simulation experiments are conducted and the results verify the improved convergence of the proposed tracking controller for UVMSs. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article

Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting. Full article