Search Results (231)

The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article

The maritime sector’s transition to sustainable energy is critical for achieving global carbon neutrality, with container terminals representing a key focus due to their high energy consumption and emissions. This study explores the potential of hydrogen energy as a decarbonization solution for port operations, using the Chuanshan Port Area of Ningbo Zhoushan Port (CPANZP) as a case study. Through a comprehensive analysis of hydrogen production, storage, refueling, and consumption technologies, we demonstrate the feasibility and benefits of integrating hydrogen systems into port infrastructure. Our findings highlight the successful deployment of a hybrid “wind-solar-hydrogen-storage” energy system at CPANZP, which achieves 49.67% renewable energy contribution and an annual reduction of 22,000 tons in carbon emissions. Key advancements include alkaline water electrolysis with 64.48% efficiency, multi-tier hydrogen storage systems, and fuel cell applications for vehicles and power generation. Despite these achievements, challenges such as high production costs, infrastructure scalability, and data integration gaps persist. The study underscores the importance of policy support, technological innovation, and international collaboration to overcome these barriers and accelerate the adoption of hydrogen energy in ports worldwide. This research provides actionable insights for port operators and policymakers aiming to balance operational efficiency with sustainability goals. Full article

Wireless power transfer systems require not only strong coupling capabilities but also stable output under various misalignment conditions. This paper proposes a hybrid magnetic coupler for autonomous underwater vehicles (AUVs), featuring two identical arc-shaped rectangular transmitting coils and a combination of an arc-shaped rectangular receiving coil and two anti-series connected solenoid coils. The arc-shaped rectangular receiving coil captures the magnetic flux generated by the transmitting coil, which is directed toward the center, while the solenoid coils capture the axial magnetic flux generated by the transmitting coil. The parameters of the proposed magnetic coupler have been optimized to enhance the coupling coefficient and improve the system’s tolerance to misalignments. To verify the feasibility of the proposed magnetic coupler, a 300 W prototype with LCC-S compensation topology is built. Within a 360° rotational misalignment range, the system’s output power maintains around 300 W, with a stable power transmission efficiency of over 92.14%. When axial misalignment of 40 mm occurs, the minimum output power is 282.8 W, and the minimum power transmission efficiency is 91.6%. Full article

Path planning for autonomous vehicles is a core component of intelligent transportation systems, playing a key role in ensuring driving safety, improving driving efficiency, and optimizing the user experience. To address the challenges of safety, smoothness, and search efficiency in path planning for autonomous vehicles, this study proposes an improved hybrid A* algorithm based on the lemming optimization algorithm (LOA). Firstly, this study introduces a penalized graph search method, improves the distance heuristic function, and incorporates the Reeds–Shepp algorithm in order to overcome the insufficient safety and smoothness in path planning originating from the hybrid A* algorithm. The penalized graph search method guides the search away from dangerous areas through penalty terms in the cost function. Secondly, the distance heuristic function improves the distance function to reflect the actual distance, which makes the search target clearer and reduces the computational overhead. Finally, the Reeds–Shepp algorithm generates a path that meets the minimum turning radius requirement. By prioritizing paths with fewer reversals during movement, it effectively reduces the number of unnecessary reversals, thereby optimizing the quality of the path. Additionally, the lemming optimization algorithm (LOA) is combined with a two-layer nested optimization framework to dynamically adjust the key parameters of the hybrid A* algorithm (minimum turning radius, step length, and angle change penalty coefficient). Leveraging the LOA’s global search capabilities avoids local optima in the hybrid A* algorithm. By combining the improved hybrid A* algorithm with kinematic constraints within a local range, smooth paths that align with the actual movement capabilities are generated, ultimately enhancing the path search capabilities of the hybrid A* algorithm. Finally, simulation experiments are conducted in two scenarios to validate the algorithm’s feasibility. The simulation results demonstrate that the proposed method can efficiently avoid obstacles, and its performance is better than that of the traditional hybrid A* algorithm in terms of the computational time and average path length. In a simple scenario, the search time is shortened by 33.2% and the path length is reduced by 11.1%; at the same time, in a complex scenario, the search time is shortened by 23.5% and the path length is reduced by 13.6%. Full article

The transportation sector in South Asia largely depends on internal combustion engine (ICE) vehicles, which are responsible for a large share of greenhouse gas (GHG) emissions, air pollution, and the increase in fuel prices. Although hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fully electric vehicles (EVs) constitute promising alternatives, the rate of their implementation is low due to factors such as the high initial investment, the absence of the required infrastructure, and the reliance on fossil fuel-based electricity. This study is the first of its kind to examine Bangladesh’s drivetrain options in a comprehensive way, with in-depth real-world emission testing and economic analysis as the main tools of investigation into the environmental and economic feasibility of different technologies used in the vehicles available in Bangladesh, including lifecycle costs and infrastructure constraints. The study findings have shown that hybrid and plug-in hybrid vehicles are the best options, since they have moderate emissions and cost efficiency, respectively. Fully electric vehicles, however, face two main challenges: the overall lack of charging infrastructure and the overall high purchase prices. Among the evaluated technologies, PHEVs exhibited the lowest environmental and economic burden. The Toyota Prius PHEV emitted 98% less NOx compared to the diesel-powered Pajero Sport and maintained the lowest per-kilometer cost at BDT 6.39. In contrast, diesel SUVs emitted 178 ppm NOx and cost 22.62 BDT/km, reinforcing the transitional advantage of plug-in hybrid technology in Bangladesh’s context. Full article

The presented research examined the impact of using biodiesel as a fuel for existing diesel engines during the transition to the broader adoption of electric vehicles powered by renewable energy or through integrated hybrid drive systems. The authors considered previous research on this topic, which is demonstrated by a literature review. This paper will utilize the findings to further explore the potential of optimizing existing engines by using biodiesel and thus propose their continued use in the transition period as one of the clean fuels. This paper outlines the standards that define fuel quality and presents a test bench equipped with an experimental engine and specialized equipment for laboratory examination, enabling the measurement of emissions and the determination of cylinder pressure. To ensure the repeatability of the experimental conditions and facilitate future comparison of the obtained results, the engine examination was conducted according to the standard ESC 13-mode test. The examination process confirmed a significant reduction in particulate matter emissions (on average 40%) but, simultaneously, an increase in nitrogen oxide emissions (on average 25%), whose level, according to data from the literature, depends on the type of raw materials used for biodiesel production. Brake thermal efficiency is higher when operating with biodiesel (on average 1.5%). Still, it was concluded that the use of biodiesel in existing diesel engines is feasible only if the engines are equipped with variable systems for automatically adjusting the compression ratio, fuel injection time, valve timing, and so on. The outcomes from the examination conducted can be further processed by applying statistical methods and represent an essential database for further research in this scientific area. Full article

The development of a robust and efficient electric vehicle (EV) charging infrastructure is essential for accelerating the transition to sustainable transportation. This systematic review analyzes recent research on EV charging network planning, with a particular focus on optimization techniques, machine learning applications, and sustainability integration. Using bibliometric methods and Principal Component Analysis (PCA), we identify key thematic clusters, including smart grid integration, strategic station placement, renewable energy integration, and public policy impacts. This study reveals a growing trend toward hybrid models that combine artificial intelligence and optimization methods to address challenges such as grid constraints, range anxiety, and economic feasibility. We provide a taxonomy of computational approaches—ranging from classical optimization to deep reinforcement learning—and synthesize practical insights for researchers, policymakers, and urban planners. The findings highlight the critical role of coordinated strategies and data-driven tools in designing scalable and resilient EV charging infrastructures, and point to future research directions involving intelligent, adaptive, and sustainable charging solutions. Full article

The long-standing need for a modern public transportation system in Lebanon, a developing country of the Middle East with an almost exclusive dependence on costly and polluting passenger cars, has become more pressing in recent years due to the worsening economic crisis and the onset of hyperinflation. This study investigates the potential reductions in energy use, emissions, and costs from the possible introduction of natural gas, hybrid, and battery-electric buses compared to traditional diesel buses in local real driving conditions. Four operating conditions were considered including severe congestion, peak, off-peak, and bus rapid transit (BRT) operation. Battery-electric buses are found to be the best performers in any traffic operation, conditional on having clean energy supply at the power plant and significant subsidy of bus purchase cost. Natural gas buses do not provide significant greenhouse gas emission savings compared to diesel buses but offer substantial reductions in the emission of all major pollutants harmful to human health. Results also show that accounting for additional energy consumption from the use of climate-control auxiliaries in hot and cold weather can significantly impact the performance of all bus technologies by up to 44.7% for electric buses on average. Performance of all considered bus technologies improves considerably in free-flowing traffic conditions, making BRT operation the most beneficial. A vehicle mix of diesel, natural gas, and hybrid bus technologies is found most feasible for the case of Lebanon and similar developing countries lacking necessary infrastructure for a near-term transition to battery-electric technology. Full article

In this study, to address the problems of the redundant safety and mass production of electro-mechanical braking (EMB) structures that are widely used in distributed drive electric vehicles (DDEV), we designed a compact dual-disc electromagnetic–EMB composite brake. The composite brake embeds an electromagnetic brake into the original friction disc, which realizes an organic combination of the friction and electromagnetic brakes. Electromagnetic braking has the advantages of no friction, a rapid response, and a high-speed braking effect, which can effectively improve the reliability and mechanical redundancy of composite braking systems. The braking system comprises regenerative, electromagnetic, and friction braking, which are typical hybrid systems. We designed a mode-switching control strategy for a composite braking system based on the hybrid control theory. MATLAB/Simulink were used to model each system and set different simulation conditions. The simulation results showed that, under different working conditions, the hybrid automata control strategy had a fast response speed, small overshoot error, and adapted to different road conditions. The feasibility of the redundant design of the electromagnetic–friction–regenerative composite braking structure and the rationality of the hybrid automata control strategy design were verified. Full article

Electric vehicles (EVs) are central to the decarbonisation of transport systems and achievement of the Sustainable Development Goals (such as SDGs 7 and 13, affordable and clean energy and climate action, respectively). This study adopts a hybrid methodological framework, merging panel econometric models with machine learning (ML), to examine the drivers of EV adoption across Africa, China, and the European Union between 2015 and 2023. We analyse the influence of charging station density (CSD), GDP per capita, renewable energy share (RES), urbanisation, and electricity access using both first-difference and fixed-effects models for causal insight and Random Forest, XGBoost, and neural network algorithms for predictive analytics. While CSD emerges as the most significant driver across models, results reveal a paradox—GDP per capita demonstrates a negative relationship with EV adoption in econometric models yet ranks among the top predictive features in ML models. This divergence highlights the limitations of assuming linear causality in high-income settings and underscores the value of combining causal and predictive approaches. SHAP and PCA analyses further illustrate regional disparities, with Africa showing low feasibility scores due to infrastructure and grid limitations. Sub-regional case studies (Kenya, South Africa, Morocco, Nigeria) emphasise the need for tailored, integrated policies that address both energy infrastructure and transport equity. Findings highlight the value of combining interpretable models with predictive algorithms to inform inclusive and region-specific EV transition strategies. Full article

In order to solve the problems of identity solidification, key duration, and lack of anonymity in communications between unmanned aerial vehicles (UAVs) and ground control stations (GCSs), a mutual secure communication scheme named Dynamic Identity and Hybrid Encryption is proposed in this paper. By constructing an identity update mechanism and a lightweight hybrid encryption system, the anonymity and untraceability of the communicating parties can be realized within a resource-limited environment, and threats such as man-in-the-middle (MITM) attacks, identity forgery, and message tampering can be effectively resisted. Dynamic Identity and Hybrid Encryption (DIHE) uses a flexible encryption strategy to balance security and computing cost and satisfies security attributes such as mutual authentication and forward security through formal verification. Our experimental comparison shows that, compared with the traditional scheme, the calculation and communication costs of DIHE are lower, making it especially suitable for the communication environment between UAVs and GCSs with limited computing power, thus providing a feasible solution for secure low-altitude Internet of Things (IoT) communication. Full article

In this paper, the concept of symmetry is utilized to optimize the structural parameters and output characteristics of the generator design—that is, the construction and solution of the equivalent magnetic circuit method for the hybrid excitation generator are symmetrical. To address the issues of high excitation loss and low power density in purely electrically excited generators, as well as the difficulty in adjusting the magnetic field in purely permanent magnet generators, a new topology for a built-in permanent magnet and salient-pole electromagnetic hybrid excitation generator is proposed. Firstly, an equivalent magnetic circuit model of the generator is established. Secondly, expressions are derived to describe the relationships between the dimensions of the salient-pole rotor and the permanent magnets and the generator’s no-load induced electromotive force, cogging torque, and air gap flux density. These expressions are then used to analyze the structural parameters that influence the generator’s performance. Thirdly, optimization targets are selected through sensitivity analysis, with the no-load induced electromotive force, cogging torque, and air gap flux density serving as the optimization objectives. A multi-objective genetic algorithm is employed to optimize these parameters and determine the optimal structural matching parameters for the generator. As a result, the optimized no-load induced electromotive force increased from 18.96 V to 20.14 V, representing a 6.22% improvement; the cogging torque decreased from 177.08 mN·m to 90.52 mN·m, a 48.88% reduction; the air gap flux density increased from 0.789 T to 0.829 T, a 5.07% improvement; and the air gap flux density waveform distortion rate decreased from 6.22% to 2.38%, a 39.3% reduction. Finally, a prototype is fabricated and experimentally tested, validating the accuracy of the simulation analysis, the feasibility of the optimization method, and the rationality of the generator design. Therefore, the proposed topology and optimization method can effectively enhance the output performance of the generator, providing a valuable theoretical reference for the design of hybrid excitation generators for vehicles. Full article

This paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational constraints. To achieve this, we employ a simheuristic algorithm for the overall route optimization, while integrating the A* algorithm to determine feasible paths between nodes that avoid obstacles in a 2D grid-based environment. Then, a feedforward neural network estimates travel time based on UAV speed, wind conditions, trajectory distance, and payload weight. This estimation is incorporated into the optimization process to improve route planning accuracy. Numerical experiments evaluate the impact of various parameters, including obstacle placement, UAV speed, wind conditions, and payload weight. These experiments include maps with 30 to 100 points of interest and varying obstacle densities and show that our hybrid method improves solution quality by up to $15\%$ in total profit compared to a baseline approach. Furthermore, computation times remain within 5–10% of the baseline, showing that the added predictive layer maintains computational efficiency. Full article

The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by the U.S. National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. This paper presents the most extensive cross-platform empirical evaluation to date of NIST-selected PQC algorithms, including CRYSTALS-Kyber and NTRU for key encapsulation mechanisms (KEMs), alongside BIKE as a code-based alternative, and CRYSTALS-Dilithium and Falcon for digital signatures. Our systematic benchmarking framework measures computational latency, memory utilization, key sizes, and protocol overhead across multiple security levels (NIST Levels 1, 3, and 5) in three distinct hardware environments and various network conditions. Results demonstrate that contemporary server architectures can implement these algorithms with negligible performance impact (<5% additional latency), making immediate adoption feasible for cloud services. In contrast, resource-constrained devices experience more significant overhead, with computational demands varying by up to 12× between algorithms at equivalent security levels, highlighting the importance of algorithm selection for edge deployments. Beyond standalone algorithm performance, we analyze integration challenges within existing security protocols, revealing that naive implementation of PQC in TLS 1.3 can increase handshake size by up to 7× compared to classical approaches. To address this, we propose and evaluate three optimization strategies that reduce bandwidth requirements by 40–60% without compromising security guarantees. Our investigation further encompasses memory-constrained implementation techniques, side-channel resistance measures, and hybrid classical-quantum approaches for transitional deployments. Based on these comprehensive findings, we present a risk-based migration framework and algorithm selection guidelines tailored to specific use cases, including financial transactions, secure firmware updates, vehicle-to-infrastructure communications, and IoT fleet management. This practical roadmap enables organizations to strategically prioritize systems for quantum-resistant upgrades based on data sensitivity, resource constraints, and technical feasibility. Our results conclusively demonstrate that PQC is deployment-ready for most applications, provided that implementations are carefully optimized for the specific performance characteristics and security requirements of target environments. We also identify several remaining research challenges for the community, including further optimization for ultra-constrained devices, standardization of hybrid schemes, and hardware acceleration opportunities. Full article

The reliable and uncertainty-aware prediction of surrounding vehicles remains a key challenge in autonomous driving. However, existing methods often struggle to quantify and incorporate uncertainty effectively. To address these challenges, we propose a hybrid architecture that combines a data-driven neural trajectory predictor with physically grounded constraints to forecast future vehicle occupancy. Specifically, the physical constraints are derived from vehicle kinematic principles and embedded into the network as additional loss terms during training. This integration ensures that predicted trajectories conform to feasible and physically realistic motion boundaries. Furthermore, a mixture density network (MDN) is employed to estimate predictive uncertainty, transforming deterministic trajectory predictions into spatial probability distributions. This enables a probabilistic occupancy representation, offering a richer and more informative description of the potential future positions of surrounding vehicles. The proposed model is trained and evaluated on the Aerial Dataset for China’s Congested Highways and Expressways (AD4CHE), which contains representative driving scenarios in China. Experimental results demonstrate that the model achieves strong fitting performance while maintaining high physical plausibility in its predictions. Full article