Search Results (226)

Surface Air-Cooled Oil Coolers (SACOCs) can become a critical component in managing the increasing thermal loads of modern turbofan engines. Installed within the bypass duct, SACOCs utilize high-mass flow bypass air for convective heat rejection, reducing reliance on traditional Fuel-Oil Heat Exchangers. This review explores SACOC design principles, integration challenges, aerodynamic impacts, and performance trade-offs. Emphasis is placed on the balance between thermal efficiency and aerodynamic penalties such as pressure drop and flow distortion. Experimental techniques, including wind tunnel testing, are discussed alongside numerical methods, and Conjugate Heat Transfer modeling. Presented studies mostly demonstrate the impact of fin geometry and placement on both heat transfer and drag. Optimization strategies and Additive Manufacturing techniques are also covered. SACOCs are positioned to play a central role in future propulsion systems, especially in ultra-high bypass ratio and hybrid-electric architectures, where traditional cooling strategies are insufficient. This review highlights current advancements, identifies limitations, and outlines research directions to enhance SACOC efficiency in aerospace applications. Full article

The rise of hybrid electric vehicles (HEVs) marks a shift away from traditional engines driven by environmental and economic concerns. With the rapid growth of HEVs worldwide, their reliability becomes of utmost concern; thus, guaranteeing the proper operation of HEVs is a crucial quest. Condition-based monitoring (CBM), which intends to observe different kinds of parameters in the system to detect defects and reduce any unwanted breakdowns and equipment failure, plays an efficient role in enhancing HEVs’ reliability and ensuring their healthy operation. The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. Hence, the condition monitoring of this machine is of great importance. The magnet crack is one of the most severe faults that may arise in this machine. Artificial intelligence (AI) is showing high capability in the field of CBM, fault detection, and fault identification and prevention. Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. Applying and evaluating various CBM methods is essential to identifying the most effective approach to maximizing reliability, minimizing downtime, and optimizing maintenance strategies. A strategy to specify the remaining useful life (RUL) of the defected element is proposed. Full article

Purely electric and hybrid vehicles are emerging as the transport sector’s response to meet climate goals, aiming to mitigate global warming. As the adoption of transport electrification increases, the importance of recycling components of the electric propulsion system at the end of their life grows, particularly the battery pack, which significantly contributes to the vehicle’s final cost and generates environmental impacts and CO₂ during production. This work presents an overview of the recycling processes for lithium-ion automotive batteries, emphasizing the developing Brazilian scenario and more established international scenarios. In Brazil, companies and research centers are investing in recycling and using reused cathode material to manufacture new batteries through the hydrometallurgical process. On the international front, pyrometallurgy and physical recycling are being applied, and other methods, such as direct processes and biohydrometallurgy, are also under study. Regardless of the recycling method, the main challenge is scaling prototype processes to meet current and future battery demand, driven by the growth of electric and hybrid vehicles, pursuing both environmental gains through reduced mining and CO₂ emissions and economic viability to make recycling profitable and support global electrification. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

The implementation of hybrid propulsion systems in vessels has gained prominence due to their significant advantages in energy efficiency and their reduction in harmful emissions, particularly during low engine load operations. This study evaluates hybrid propulsion system applications in two different tugboats, focusing on fuel consumption and engine load across eight distinct operational scenarios, including Istanbul Strait crossings and towing and pushing manoeuvres. The scenarios incorporate asynchronous electric motors with varying power ratings, lead-acid and lithium iron phosphate batteries with distinct storage capacities, and photovoltaic panels of different sizes. The highest fuel savings of 72.4% were recorded in the second scenario, which involved only towing and pushing operations using lithium iron phosphate batteries. In contrast, the lowest fuel savings of 5.2% were observed in the sixth scenario, focused on a strait crossing operation employing lead-acid batteries. Although integrating larger-scale batteries into hybrid propulsion systems is vital for extended ship operations, their adoption is often limited by space and weight constraints, particularly on tugboats. Nevertheless, ongoing advancements in hybrid system technologies are expected to enable the integration of larger, more efficient systems, thereby enhancing fuel-saving potential. Full article

The intrinsic hazards associated with high-pressure hydrogen, combined with electromechanical interactions in hybrid architectures, pose significant challenges in predicting potential system risks during the conceptual design phase. In this paper, a risk analysis methodology integrating systems theoretic process analysis (STPA), D-S evidence theory, and Bayesian networks (BN) is established. The approach employs STPA to identify unsafe control actions and analyze their loss scenarios. Subsequently, D-S evidence theory quantifies the likelihood of risk factors, while the BN model’s nodal uncertainties to construct a risk network identifying critical risk-inducing events. This methodology provides a comprehensive risk analysis process that identifies systemic risk elements, quantifies risk probabilities, and incorporates uncertainties for quantitative risk assessment. These insights inform risk-averse design decisions for hydrogen–electric hybrid powered aircraft. A case study demonstrates the framework’s effectiveness. The approach bridges theoretical risk analysis with early-stage engineering practice, delivering actionable guidance for advancing zero-emission aviation. Full article

Fast patrol boats account for a large number among the numerous vessels used in naval fleets. Owing to their operational characteristics, which involve relatively high speeds, they contribute to emissions significantly. This study presents an optimized design concept for a diesel–battery hybrid electric propulsion system integrated into the general ship design process for fast patrol boats. The optimization design uses mixed-integer linear programming to determine the most eco-friendly shares ratio of battery and diesel usage while satisfying high-endurance operational scenarios. A shares ratio of 1.259 tons of diesel to 2.88 tons of batteries was identified as the most eco-friendly configuration capable of meeting a 200-nautical-mile operational scenario at a maximum speed of 35 knots for the selected case study. A quantitative comparison through a global warming potential (GWP) analysis was conducted between conventional diesel propulsion systems and the designed diesel–battery hybrid electric propulsion system, using a life-cycle assessment (LCA) standardized under the ISO framework. The analysis confirmed that the optimized hybrid propulsion system can achieve a GWP reduction of approximately 7–9% compared with conventional propulsion systems. Few studies have applied LCA in this field, and the application of batteries as hybrid secondary energy sources is viable and sustainable for high-endurance scenarios. Full article

Unmanned aerial vehicles (UAVs) are becoming a major part of the civil and military aviation industries. They meet user needs for effective supply transportation and the real-time acquisition of accurate information during air operations. Recently, concerns about greenhouse gas (GHG) emissions have increased due to the use and depletion of fossil fuels, shifting attention toward the broader use of electric propulsion as a green technology in different sectors, including transportation. The long-term objective of this work is to build a prototype of a hybrid electric propulsion system (HEPS) dedicated to a multi-rotor unmanned aerial vehicle with a MTOW of 25 kg and an onboard electric voltage of 44.4 V. The main components and operating principles of the HEPS were defined. The main HEPS digital twin block modules and their operations were described. Using the developed digital twin structure and operational model, simulations were carried out. Based on the results, it can be demonstrated that the use of hybrid electric propulsion allows for a significant increase in the flight time of a multi-rotor UAV. The developed DT can be used as a tool for optimizing the operation of the HEPS prototype and for redefining mathematical models of individual components. Full article

Electric and hybrid marine vessels are marking a new phase of eco-friendly maritime transport, combining electricity and traditional propulsion to boost efficiency and reduce emissions. The industry’s advancements in charging infrastructure and strict regulations help these vessels lead the way toward a sustainable and economically viable future in shipping. In this review, electric and hybrid marine vessels are discussed, including past applications and trend demonstrations. This paper systematically analyzes maritime vessels’ energy management and battery systems, highlighting advances in lithium-based and alternative battery technologies. Additionally, the review examines the impact of these technologies on sustainability and operational efficiency in the maritime industry. This paper contributes to the field by presenting a holistic view of the challenges and solutions associated with the electrification of maritime vessels, aiming to inform future developments and policymaking in this dynamic sector. Unlike many existing reviews that focus exclusively on battery chemistries or energy management algorithms, this manuscript integrates multiple aspects of maritime electrification—including propulsion types, charging infrastructure, grid systems (MVDC), EMS, BMS, and AI applications—into one cohesive systems-level review. This cross-sectional integration is particularly rare in the literature and enhances the practical value of the review for designers, policymakers, and shipbuilders. Full article

In the context of the new energy hybrid ship propulsion system (NE-HSPS), the parameters of the rotor speed, torque, and current of the permanent magnet synchronous motor (PMSM) are susceptible to environmental variations and unmodeled disturbances. Conventional nonlinear controllers (e.g., backstepping, PI, and sliding mode) encounter challenges related to response speed, interference immunity, and vibration jitter. These challenges stem from the inherent uncertainties in perturbations and the limitations of the traditional nonlinear controllers. In this paper, a novel Adaptive Finite-Time Command-Filtered Backstepping Controller (AFTCFBC) is proposed, featuring a faster response time and the elimination of overshoot. The proposed controller is a significant advancement in the field, addressing the computational complexity of backstepping control and reducing the maximum steady-state error of the control output. The novel controller incorporates a Nonlinear Finite-Time Command Filter (NFTCF) adapted to the variation in motor speed. Secondly, a novel Nonlinear Sliding Mode Observer (NSMO) is proposed based on the designed nonlinear sliding mode gain function (φ(S_w)) to estimate the load disturbance of the electric propulsion system. The Uncertainty Parameter-Adaptive law (UPAL) is designed based on Lyapunov theory to improve the robust performance of the system. The construction of a simulation model of a hybrid ship PMSM under four distinct working conditions, including constant speed and constant torque, the lifting and lowering of speed, loading and unloading, and white noise interference, is presented. The results of this study demonstrate a significant reduction in speed-tracking overshoot to zero, a substantial decrease in integral squared error by 90.15%, and a notable improvement in response time by 18.6%. Full article

Due to the limitations of pure electric power endurance, turbo-electric hybrid power systems, which offer a high power-to-weight ratio, present a reliable solution for medium- and large-sized vertical take-off and landing (VTOL) aircraft. Traditional energy management strategies often fail to minimize fuel consumption across the entire flight profile while meeting power demands under varying flight conditions. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based energy management strategy (EMS) specifically designed for turbo-electric hybrid propulsion systems. Firstly, the proposed strategy employs a Prior Knowledge-Guided Deep Reinforcement Learning (PKGDRL) method, which integrates domain-specific knowledge into the Deep Deterministic Policy Gradient (DDPG) algorithm to improve learning efficiency and enhance fuel economy. Then, by narrowing the exploration space, the PKGDRL method accelerates convergence and achieves superior fuel and energy efficiency. Simulation results show that PKGDRL has a strong generalization capability in all operating conditions, with a fuel economy difference of only 1.6% from the offline benchmark of the optimization algorithm, and in addition, the PKG module enables the DRL method to achieve a huge improvement in terms of fuel economy and convergence rate. In particular, the prospect theory (PT) in the PKG module improves fuel economy by 0.81%. Future research will explore the application of PKGDRL in the direction of real-time total power prediction and adaptive energy management under complex operating conditions to enhance the generalization capability of EMS. Full article

Unmanned aerial vehicles, known today as drones, in the beginning, were small-dimension research models powered by small electric motors fed from electrical batteries. The propulsion system for these drones had to be adapted to the specific applications along their development. Electric and hybrid-electric propulsion drones represent a rapidly developing field in the aerospace industry. Electric drones are those with purely electric propulsion fed from batteries, while hybrid-electric ones have a hybrid propulsion system combining a thermal engine and an electric motor. Another class of hybrid-electric drones includes those with an electric propulsion system fed from fuel cells and batteries. This paper proposes the configuration of an electric propulsion system with a hybrid power source for a transport drone, as well as an analysis of the special electrical components onboard an electric drone, such as batteries, fuel cells, and electric motors. In the final part of the paper, this propulsion system is modeled and analyzed in Matlab/Simulink version 2021a. Design software and simulation tools specifically developed for hybrid-electric drones are essential for ensuring the accuracy and efficiency of these processes. Electric drones have the advantage of zero emissions, but at present, the batteries are still too heavy for aviation applications. By using hydrogen fuel cells as the main power source, it is possible to considerably reduce the power source weight. This is an important advantage of the system proposed in this work. Using hydrogen fuel cells in aircraft and drone propulsion is an important trend in the scientific world. This technology seems to be mature enough to be implemented in aviation. From a technical point of view, these kinds of systems are already feasible. Their usefulness and reliability have to be proven in time. Full article

This study presents an AI-driven energy management system (EMS) for a hybrid electric flying car, integrating multiple power sources—including solid-state batteries, Li-ion batteries, fuel cells, solar panels, and wind turbines—to optimize power distribution across various flight phases. The proposed EMS dynamically adjusts power allocation during takeoff, cruise, landing, and ground operations, ensuring optimal energy utilization while minimizing losses. A MATLAB-based simulation framework is developed to evaluate key performance metrics, including power demand, state of charge (SOC), system efficiency, and energy recovery through regenerative braking. The findings show that by optimizing renewable energy collecting, minimizing battery depletion, and dynamically controlling power sources, AI-based predictive control dramatically improves energy efficiency. While carbon footprint assessment emphasizes the environmental advantages of using renewable energy sources, SOC analysis demonstrates that regenerative braking prolongs battery life and lowers overall energy use. AI-optimized energy distribution also lowers overall operating costs while increasing reliability, according to life-cycle cost assessment (LCA), which assesses the economic sustainability of important components. Sensitivity analysis under sensor noise and environmental disturbances further validates system robustness, demonstrating that efficiency remains above 84% even under adverse conditions. These findings suggest that AI-enhanced hybrid propulsion can significantly improve the sustainability, economic feasibility, and real-world performance of future flying car systems, paving the way for intelligent, low-emission aerial transportation. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in both civilian and military applications around the world. There are several types of UAVs with classifications according to several quantities. Medium-Altitude Long-Endurance (MALE) UAVs comprise one of these classifications. Hybrid or electric propulsion systems are another topic that is becoming popular. Implementing electric propulsion systems in vehicles could result in more efficient, environmentally friendly, and improved systems in comparison with conventional systems. This concept can be seen in the automotive sector, and today, it is popular in the aviation sector. Based on a literature review, full-electric concepts are often applied to some classes of UAVs. MALE-class UAVs are often used with conventional propulsion systems, as they need a long endurance during flight. It is known that current battery technologies and weight limitations on board do not allow as long of a flight time as conventional systems. Even knowing this, there could be some advantages to choosing an electric propulsion system in MALE-class UAVs. The effects and performance of electric propulsion in MALE-class UAVs were studied with a newly designed electric machine and a newly created UAV model. Full article

The global adoption of battery electric vehicles (EVs) and hybrid electric vehicles (HEVs) as a substitute for internal combustion engine cars (ICEs) in various nations offers a substantial opportunity to reduce carbon dioxide (CO₂) emissions from land transportation. EVs are fitted with an energy conversion system that efficiently converts stored energy into propulsion, referred to as “tank-to-wheel (TTW) conversion”. Battery-electric vehicles have a significant advantage in that their exhaust system does not produce any pollutants. This hypothesis is equally relevant to public transport. Despite their higher upfront cost, electric buses contribute significantly to environmental sustainability during their operation. This study aimed to evaluate the environmental sustainability of electric buses during their operational phase by utilizing the life cycle assessment (LCA) technique. This paper used the MATLAB R2021b code to ascertain the mean load of the buses during their operation. The energy consumption of battery electric and hybrid electric buses was evaluated using the WLTP Class 2 standard, which refers to vehicles with a power-to-mass ratio between 22 and 34 W/kg, overing four speed phases (low, medium, high, extra high) with speeds up to 131.3 km/h. The code was used to calculate the energy consumption levels for the complete test cycle. The code adopts an idealized rectangular blind box model, disregarding the intricate design of contemporary buses to streamline the computational procedure. Simulating realistic test periods of 1800 s resulted in an average consumption of 1.451 kWh per km for electric buses and an average of 25.3 L per 100 km for hybrid buses. Finally, through an examination of the structure of the Hungarian power system utilization, it was demonstrated that electrification is a more appropriate method for achieving the emission reduction goals during the utilization phase. Full article