Vehicle Technology Research Articles

A bi-subpopulation coevolutionary immune algorithm for multi-objective combinatorial optimization in multi-UAV task allocation

With the development of Unmanned Aerial Vehicle (UAV) technology towards multi-UAV and UAV swarm, multi-UAV cooperative task allocation has more and more influence on the success or failure of UAV missions. From the operational research point of view, such problems belong to high-dimensional combinatorial optimization problems, which makes the solving process face many challenges. One is that the discrete and high-dimensional decision variables make the quality of the solution obtained with acceptable time not guaranteed. Second, the desired solution of real missions often needs to satisfy multiple objective functions, or a set of solutions for decision-making. Therefore, this paper constructs a Multi-objective Combinatorial Optimization in Multi-UAV Task Allocation Problem (MCOTAP) model, and proposes a Bi-subpopulation Coevolutionary Immune Algorithm (BCIA). The two coevolutionary mechanisms improve the lower limit of population diversity, and the evolutionary strategy pool integrating multiple strategies and the adaptive strategy selection mechanism enhance the local search ability in the late evolution. In the experiments, BCIA competes fairly with the mainstream multi-objective evolutionary algorithms (MOEAs), multi-objective immune algorithms (MOIAs) and the recently proposed multi-UAV mission planning algorithms. The experimental results on different test problems (including several multi-objective combinatorial optimization benchmark problems and the proposed MCOTAP model) show that BCIA has superior performance in solving multi-objective combinatorial optimization problems (MCOPs). At the same time, the effectiveness of each design component of BCIA has been comprehensively verified in the ablation study.

Multi-dimensional Analysis and Strategy of the Development of New Energy Vehicles

With the global emphasis on environmental protection and sustainable development, new energy vehicles have become crucial in addressing issues such as the over-exploitation of non-renewable resources, climate change, and reducing dependence on traditional fossil fuels. Currently, new energy vehicle technologies are being innovated and the market scale is expanding. However, there are also problems such as limited driving range, insufficient charging facilities, and the lack of unified standards for vehicle performance and safety. This paper focuses on the current situation of the development of new energy vehicles and puts forward suggestions for future development. This research is structured around three key areas: new energy technologies, infrastructure, and market promotion. Using a combination of qualitative analysis, case analysis, and comparative analysis, this paper explores technological breakthroughs, with a focus on improving battery performance and developing new battery technologies; infrastructure development, emphasizing the layout and expansion of charging facilities; and market promotion, which addresses consumer demands and marketing strategies.

The Future of Last-Mile Delivery: Lifecycle Environmental and Economic Impacts of Drone-Truck Parallel Systems

With rapid advancements in unmanned aerial vehicle (UAV) technology, its integration into logistics operations has emerged as a promising solution for improving efficiency and sustainability. Among the emerging solutions, a collaborative delivery model involving drones and trucks addresses last-mile delivery challenges by leveraging the complementary strengths of both modes of transport. However, evaluating the environmental and economic impacts of this transportation mode requires a systematic framework to capture its unique characteristics and minimize environmental impacts and costs. This paper investigates the Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP) to evaluate the environmental and economic sustainability of a collaborative drone-truck delivery system. Specifically, a mathematical model for this delivery system is developed to optimize joint delivery operations. Environmental impacts are assessed using a comprehensive Life Cycle Assessment (LCA), including emissions and operational noise, while a Life Cycle Cost Analysis (LCCA) quantifies economic performance across five cost dimensions. Sensitivity analysis explores factors such as delivery density, traffic congestion, and wind conditions. Results show that, compared to the electric vehicle fleet, the proposed model achieves an approximate 20% reduction in carbon emissions, while delivering a 20–30% cost reduction relative to the fuel truck fleet. Drones’ efficiency in short-distance deliveries alleviates trucks’ load, cutting environmental and operational costs. This study offers practical insights and recommendations for implementing drone-truck parallel delivery systems, particularly in high-demand density areas.

Advancements in Autonomous Driving Systems: Enhancing Environmental Perception with Lidar Integration and Innovative Algorithms

This paper discusses the intricacies of autonomous driving systems, focusing on the four core modules: perception, sensing, decision-making, and control. The sensing module integrates various sensors like LiDAR, cameras, millimeter-wave radars, IMU, and GPS to gather essential data. The paper emphasizes the role of LiDAR in 3D object detection and point cloud segmentation, which are crucial for environmental perception. It also addresses the challenge of fusing data from multiple sensors into a unified coordinate system for effective decision-making. This research presents several significant advances in environmental perception. First there is a single-stage 3D object detection algorithm using sparse convolutional neural networks for improved speed and accuracy. Moreover there is a two-stage 3D object detection method focuses on detecting difficult targets through target center prediction. This method employs a novel neural network architecture, AOAP-SCNN, for feature extraction. Furthermore there is a point cloud instance segmentation algorithm (a method that separates and identifies individual objects in 3D space) utilizes a front view approach and a DLA network with Tiny-PointNet for feature extraction. Finally there is also an automatic calibration algorithm for LiDAR and camera systems based on segmentation, designed for real-time operation on embedded platforms, which corrects the extrinsic matrix and improves the self-calibration system's practical value. Building upon these technical innovations, the paper provides a comprehensive analysis of intelligent vehicle technology including the history of development, the importance of environmental perception, and the application and limitations of LiDAR in intelligent vehicles.

What Impact Does Net Zero Action on Road Transport and Building Heating Have on Exposure to UK Air Pollution?

This study explores the cobenefits of reduced nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM), through net zero (NZ) climate policy in the UK. Two alternative NZ scenarios, the balanced net zero (BNZP) and widespread innovation (WI) pathways, from the UK Climate Change Committee's Sixth Carbon Budget, were examined using a chemical transport model (CTM). Under the UK existing policy, Business as Usual (BAU), reductions in NO2 and PM were predicted by 2030 due to new vehicle technologies but plateau by 2040. The BNZP and WI scenarios show further reductions particularly by 2040, driven by accelerated electric vehicle (EV) uptake and low-carbon heating in buildings, with the building contribution to PM reduction being 2-3 times greater than road transport. The results demonstrate that the NZ transition to EVs (cars and vans) reduces both exhaust and nonexhaust emissions, as well as reducing traffic volumes. O3 trends are complex with a small overall increase by 2030 and a decrease by 2040. Although uncertain, 2050 predictions of BNZP showed important additional air pollution benefits. Our findings highlight the efficacy of NZ strategies, providing insights for UK and international policymakers interested in the air pollution cobenefits of climate policy.

Based on AVL-CRUISE Hybrid and Electric Vehicle Simulation and FTP-72 Circle Power Consumption Factor Analysis

With the rapid advancement of technology and the growing scarcity of energy resources, finding effective ways to reduce power consumption has become increasingly crucial. Hybrid and electric vehicles play a vital role in this endeavor, warranting deeper exploration and focus. In this study, I utilized AVL-Cruise software, a powerful tool for simulation and data modeling, to conduct comprehensive simulations of hybrid and electric vehicle performance, with a specific focus on analyzing the FTP72 cycle.The simulation results are highly encouraging. The vehicle achieved a maximum speed of 164 km/h, an acceleration rate of 3 m/s, a 0 to 100 km/h acceleration time of 9 seconds, and a maximum climbing slope of approximately 74%. These figures not only demonstrate that hybrid and electric vehicles can meet performance expectations but also highlight their capability to deliver on key metrics. Furthermore, the total energy consumption was recorded at 4200 kJ, underscoring the efficiency of these vehicles.In addition to the simulation outcomes, I delved into various factors that influence vehicle performance, including vehicle mass, drag coefficient, and motor power.Using MATLAB, I conducted a detailed analysis of the relationships between these factors and energy consumption, incorporating residual analysis and deriving a mathematical equation to describe these interactions.Through this extensive data analysis and model fitting, we gain a deeper understanding of hybrid and electric vehicle dynamics. This research not only contributes to the field but also has the potential to drive further development, broadening the focus and scope of hybrid and electric vehicle technology

Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs.

Analysis of wireless charging technology for electric vehicles based on comparison of dual LCL-type structure and PP-type structure

Analysis of wireless charging technology for electric vehicles based on comparison of dual LCL-type structure and PP-type structure

Decarbonizing the Transport of Microalgae-based Products —The Role of E-mobility

Background: The decarbonization of road transport is a precondition for achieving carbon neutrality. Battery-electric vehicle technology, driven by several patents, can make this a reality. In this bias, the objective of the article is to shed light on the ongoing debate about the potentially important role of the adoption of electric vehicles in the transport of microalgae-based products to help them advance to a cleaner life cycle. Methods: Five routes, including unimodal and multimodal conditions, were defined to assess the carbon emissions of the transport system and, more specifically, of road transport. The headquarters of market-leading microalgae manufacturers were selected as the origin of the routes and, as the destination, regions that sustain them. Results: The results reveal the supremacy of road transport of microalgae-based products using electric vehicles powered by nuclear, hydroelectric, and wind, followed by biomass and photovoltaic energy. They also show that the positive impact of wind, water, and photovoltaic energy on the climate, added to the lower battery charging costs and the greater opportunity to generate revenue from the sale of carbon credits, make their trade-offs. Conclusion: The exquisite results of this study convey key messages to decision-makers and stakeholders about the role of electromobility in building a zero-carbon delivery route.

Investigating the impacts of connected vehicle technology on the flow of trucks at the busiest Canada-U.S. border crossings

Investigating the impacts of connected vehicle technology on the flow of trucks at the busiest Canada-U.S. border crossings

Preparation of LiDAR-detectable Black Pigments via Recycling the Silicon Sludge Generated from the Semiconductor Manufacturing Processes

A novel LiDAR-detectable plate-like hollow black titanium dioxide (HbTiO2) is developed by recycling silicon sludge generated from silicon wafer sawing. By employing TiCl4 sol-gel synthesis, hydrofluoric acid etching, and NaBH4 reduction, the hollow-structured black TiO2 is successfully synthesized. Plate-like HbTiO2 readily mixed with hydrophilic varnish, owing to its inherent hydrophilic properties. With monolayer coating, HbTiO2-based paints exhibit the blackness (L* = 17.63) comparable to that of commercial black paints, indicating that NaBH4 successfully changed the color of TiO2 from white to black. In addition to its blackness, HbTiO2 exhibits a superior near-infrared (NIR) reflectance of ca. 26.8R% at 905nm, making it suitable for integration with the LiDAR systems used in autonomous vehicles. This high NIR reflectance ensures that HbTiO2 can effectively interact with the LiDAR sensors, attributing to the hollow structures and effective light reflection mechanism. Furthermore, the use of recycled silicon sludge not only offers a cost-effective alternative to traditional template materials but also promotes environmental sustainability by reducing solid waste. Our findings demonstrate the potential of HbTiO2 as an innovative and practical LiDAR-detectable black pigment, paving the way for advanced applications in autonomous vehicle technologies.

Real-time lane detection for autonomous vehicles using YOLOV5 Segmentation Model

ABSTRACT The increasing prominence of autonomous vehicles underscores the critical need for precise and prompt lane detection to ensure safe and reliable transportation. This paper aims to enhance autonomous vehicle performance by developing an advanced real-time lane detection system using the YOLOV5 Segmentation Large Model. The methodology utilises LabelMe for dataset annotation and employs rigorous pre-processing techniques such as resizing, augmentation, noise addition, greyscale conversion, enhanced exploration size, and image rotation. YOLOv5 architecture is extensively employed for in-lane instance segmentation. The proposed model achieves high performance with a pixel accuracy of 89%, a processing speed of 48 frames per second, and mAP@0.5 scores of 0.885 for the bounding box and 0.731 for the mask. These results demonstrate the model’s effectiveness in various conditions. This paper introduces an innovative approach to autonomous vehicle lane detection, leveraging the YOLOV5 Segmentation Large Model for unparalleled precision and recall rates. This paper makes a significant contribution to the advancement of autonomous vehicle technology, emphasising the critical role of reliable and timely lane detection in strengthening the dependability and safety of transportation systems.

AI-Enhanced Automatic Traffic Violation Detection and Challan Issuance using GNSS and OBU Sensors in Self-Driving Cars.

The swift progress in self-driving vehicle technology requires strong systems to guarantee adherence to traffic regulations. This study introduces an AI-powered system for automatic detection of traffic violations and issuance of fines in self-driving vehicles utilizing Global Navigation Satellite System (GNSS) and On-Board Unit (OBU) sensors. The system combines AI algorithms with GNSS for accurate geolocation tracking and OBU sensors for monitoring vehicle status. Infractions like running signals, exceeding speed limits, and improper use of lanes are identified instantly. A main server analyzes violation data and automatically generates electronic fines for offenders. The suggested system improves adherence to traffic regulations, minimizes human involvement, and fosters safer roadways by utilizing AI's predictive and analytical features. This novel strategy tackles shortcomings in current traffic enforcement techniques, rendering it a scalable answer for contemporary smart cities. Keywords - Autonomous Vehicles, Traffic Violation Detection, GNSS, OBU Sensors, AI, Smart Cities, Electronic Challan, Traffic Compliance

Generative AI Adoption in Automotive Vehicle Technology: Case Study of Custom GPT

Generative Artificial Intelligence (GenAI) is revolutionizing the automotive industry by introducing transformative applications in areas such as autonomous driving, vehicle maintenance, and in-car assistance.

A novel development of optimized hybrid MPPT controller for fuel cell systems with high voltage transformation ratio DC–DC converter

The world is moving towards the utilization of hydrogen vehicle technology because its advantages are uniformity in power production, more efficiency, and high durability when compared to fossil fuels. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) device is selected for producing the energy for the hydrogen vehicle. The merits of this fuel technology are the possibility of operating less source temperature, and more suitability for stationery and transportation applications. Also, it provides a high amount of power density for heavy-duty electric vehicle applications. However, the major issue of the fuel stack technology is excessive current generation. Here, in the first objective, a Single Switch Wide Voltage Supply Converter (SSWVSC) is proposed to optimize the current levels of the fuel device thereby reducing the energy conduction losses of the entire system. In the 2nd objective, the duty cycle generation for the converter and handling of nonlinear energy generation of the fuel device has been done by introducing the Greywolf Optimization-dependent Adaptive neuro-fuzzy inference system (ANFIS). The features of this hybridization concept are less iteration number needed, less disturbance in MPP position, low stabilizing time of the fuel module production voltage, and more reliability. Here, the fuel module interfaced DC-DC circuit is studied by utilizing the MATLAB software and the introduced converter is tested only with programmable DC-Source.

Technology for geostructural forecasting of gold ore occurrences using the example of a section of the Verkhoyansk-Kolyma fold system (Bayag ore field, Yakutia)

Relevance. Additional exploration of Bayag gold-ore deposit (Republic of Sakha, Yakutia), requiring the localization of targets predicted for gold-sulphide occurrences of endogenous (hydrothermal) nature. Aim. Further approbation and finalizing of representative technology for interpretation of aeromagnetic survey results, using domestic unmanned aerial vehicle technology. In the context of the mentioned geological object and research goals, the research methods include the combined analysis of remote-sensing, geomorphological and aeromagnetic data based on the methods of qualitative and quantitative interpretations, which are invariant with regard to the kind of initial signal, as well as it is based on the direct and indirect forecast parameters. Used in this research interpretation methods are not dependent on the nature of initial scalar field, being measured instrumentally, and include relatively automated approaches for decoding the morphostructure of this field, its filtration and consideration of the relationship in the set of such fields of different genesis. The direct forecast parameters are supposed to be the extrapolation of reference sample within the experimental area. Indirect forecast criteria are related to pattern recognition without training and, besides the well-known classifications, they include the recalculations into quasi-elastic indicators of mining block – fragmentation parameter and stress concentrator position parameter. Results. Reduced to justifying the applicability of the method, tested by the authors, for extrapolation of quasi-periodic reference sample within the experimental area, based on pattern recognition with training and subsequent verification by geochemical estimation. The content of the conclusions is: 1) optimization of mapped target contours localized within ore-controlling tectonic zones; 2) possibility of applying predictive use of the property of quasi-periodic manifestation of structural-substantial anomalies as special case of wave structuring of the mining massif.

Electric Aviation: Pioneering the Future through Advances in Electric Vehicle Technologies

This paper explores the technological advancements in electric vehicles (EVs) and their potential applications in electric aviation. Core EV technologies such as battery systems, electric motors, and electronic control systems are analyzed to understand their relevance to aviation. The evolution of various battery types, including lithium-ion, solid-state, and next-generation alternatives, is emphasized, focusing on their critical role in addressing energy density challenges that electric aircraft will face. Additionally, this paper discusses the technical, regulatory, and manufacturing hurdles that must be overcome for electric aviation to become commercially viable. From battery weight and endurance limitations to energy-efficient propulsion systems, the complexities of translating EV innovations to aviation are assessed. The paper concludes by examining future possibilities for electric aviation, highlighting how breakthroughs in EV technology could lead to sustainable air travel. Through this lens, the technological trajectory of EVs provides key insights into the opportunities and challenges of developing electric aircraft.

Sensor Application: Introducing Autonomous Vehicle Technology in Loading Vehicles to a Pure Car and Truck Carrier

Sensor Application: Introducing Autonomous Vehicle Technology in Loading Vehicles to a Pure Car and Truck Carrier

Optimization of Energy Management Strategy for Series Hybrid Electric Vehicle Equipped with Dual-Mode Combustion Engine Under NVH Constraints

Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications.

Exploration of ultra-low carbon heavy-duty commercial vehicle technology: Liquid ammonia-diesel dual fuel high-pressure direct injection

Ammonia (NH3) has attracted substantial interest as a potential carbon-free fuel source for engine applications. The high-pressure ammonia direct injection is believed a way for higher efficiency. However, only a limited number of experimental studies have been conducted on direct-injection ammonia-fueled engines. In this study, a novel ammonia-diesel dual direct-injection compression-ignition engine was constructed and tested under high-load conditions. The results demonstrated that ammonia fuel combustion was advantageous in high-load situations, achieving a high carbon reduction of 75%. The indicated thermal efficiency (ITE) could be increased to 51.6% at an indicated mean effective pressure (IMEP) of 14 bar by employing a diesel pilot-injection strategy and increasing the pilot-injection ratio to 98%. Moreover, the diesel pilot-injection strategy at high load conditions also decreased unburned ammonia and nitrous oxide emissions. The lowest NH3 and N2O emissions were reduced to 2.1 g/kWh and 0.67 g/kWh. Under high-load conditions of IMEP = 16 bar, employing a diesel pilot-injection strategy increased AER to 73% while maintaining high efficiency. When AER was further increased to 84%, the ITE showed a noticeable decline, and unburned ammonia emissions increased accordingly.