Frequency: Quarterly E- ISSN: 2277-8292 P- ISSN: Awaited Abstracted/ Indexed in: Ulrich's International Periodical Directory, Google Scholar, SCIRUS, getCITED, Genamics JournalSeek, EBSCO Information Services
"Inventi Impact: Vehicular Technology" is a peer reviewed journal of Engineering & Technology. The journal provides publishing space for the research and review papers and reports related to all the areas of vehicular technology including communications i.e. use of mobile radio on land, sea and air, transportation systems including walkways or people movers, and vehicular electronics. \nThe journal is open to negative reports and provides special consideration for the articles pertaining to mass transit system, system solving the issues of underdeveloped or provincial communities and the ones putting lesser toll on the environment.
This paper presents a new hybrid cascaded H-bridge multilevel inverter motor drive DTC scheme for electric vehicles where each\r\nphase of the inverter can be implemented using a single DC source. Traditionally, each phase of the inverter requires ?? DC source\r\nfor 2?? + 1 output voltage levels. In this paper, a scheme is proposed that allows the use of a single DC source as the first DC source\r\nwhich would be available from batteries or fuel cells, with the remaining (?? - 1) DC sources being capacitors. This scheme can\r\nsimultaneously maintain the capacitors of DC voltage level and produce a nearly sinusoidal output voltage due to its high number\r\nof output levels. In this context, high performances and efficient torque and flux control are obtained, enabling a DTC solution for\r\nhybrid multilevel inverter powered induction motor drives intended for electric vehicle propulsion. Simulations and experiments\r\nshow that the proposed multilevel inverter and control scheme are effective and very attractive for embedded systems such as\r\nautomotive applications....
Faced with the charging difficulties of free-floating shared electric vehicles and the high cost of single-demand mobile charging, this paper proposes a cooperative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the system charging total cost minimization model, which searches for the optimal charging scheme and number of mobile charging vehicles. The lower level model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for the mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% when compared to the fixed-only charging scheme, and by 14.9% when compared to the mobile-only charging scheme....
To address the challenges of missed detections caused by insufficient shape and texture features and blurred boundaries in existing detection methods, this paper introduces a novel moving vehicle detection approach for satellite videos. The proposed method leverages frame difference and convolution to effectively integrate spatiotemporal information. First, a frame difference module (FDM) is designed, combining frame difference and convolution. This module extracts motion features between adjacent frames using frame difference, refines them through backpropagation in the neural network, and integrates them with the current frame to compensate for the missing motion features in single-frame images. Next, the initial features are processed by a backbone network to further extract spatiotemporal feature information. The neck incorporates deformable convolution, which adaptively adjusts convolution kernel sampling positions, optimizing feature representation and enabling effective multiscale information integration. Additionally, shallow large-scale feature maps, which use smaller receptive fields to focus on small targets and reduce background interference, are fed into the detection head. To enhance small-target feature representation, a small-target self-reconstruction module (SR-TOD) is introduced between the neck and the detection head. Experiments using the Jilin-1 satellite video dataset demonstrate that the proposed method outperforms comparison models, significantly reducing missed detections caused by weak color and texture features and blurred boundaries. For the satellite-video moving vehicle detection task, this method achieves notable improvements, with an average F1-score increase of 3.9% and a per-frame processing speed enhancement of 7 s compared to the next best model, DSFNet....
To analyze the psychological impacts of the introduction of new portable electric transportation modes, we implemented an\r\nexperiment using a personal mobile vehicle (PMV). We investigated its effects on 2 types of the subjective quality of mobility\r\n(SQM): instrumental aspects including ââ?¬Å?easinessââ?¬Â and ââ?¬Å?speedââ?¬Â: and affective aspects including ââ?¬Å?enjoyment,ââ?¬Â ââ?¬Å?seeing scenery,ââ?¬Â and\r\nââ?¬Å?enjoying the atmosphere.ââ?¬Â The result indicated that PMV might contribute to the improvement of the instrumental aspects\r\nof SQM, but walking was regarded as more preferable in terms of the affective aspects. The results suggest that such a new\r\ntransportation mode could contribute to the improvement of subjective quality of mobility, if and only if it can be introduced\r\nin an appropriate situation....
Many leading companies in the automotive industry have been putting tremendous effort into developing new powertrains and technologies to make their products more energy efficient. Evaluating the fuel economy benefit of a new technology in specific powertrain systems is straightforward; and, in an early concept phase, obtaining a projection of energy efficiency benefits from new technologies is extremely useful. However, when carmakers consider new technology or powertrain configurations, they must deal with a trade-off problem involving factors such as energy efficiency and performance, because of the complexities of sizing a vehicle’s powertrain components, which directly affect its energy efficiency and dynamic performance. As powertrains of modern vehicles become more complicated, even more effort is required to design the size of each component. This study presents a component-sizing process based on the forward-looking vehicle simulator “Autonomie” and the optimization algorithm “POUNDERS”; the supervisory control strategy based on Pontryagin’s Minimum Principle (PMP) assures sufficient computational system efficiency. We tested the process by applying it to a single power-split hybrid electric vehicle to determine optimal values of gear ratios and each component size, where we defined the optimization problem as minimizing energy consumption when the vehicle’s dynamic performance is given as a performance constraint. The suggested sizing process will be helpful in determining optimal component sizes for vehicle powertrain to maximize fuel efficiency while dynamic performance is satisfied. Indeed, this process does not require the engineer’s intuition or rules based on heuristics required in the rule-based process....
This paper focuses on routing for vehicles getting access to infrastructure either directly or viamultiple hops through other vehicles.\nWe study routing protocol for low-power and lossy networks (RPL), a tree-based routing protocol designed for sensor networks.\nMany design elements from RPL are transferable to the vehicular environment. We provide a simulation performance study of\nRPL and RPL tuning in VANETs. More specifically, we seek to study the impact of RPL�s various parameters and external factors\n(e.g., various timers and speeds) on its performance and obtain insights on RPL tuning for its use in VANETs. We then fine tune\nRPL and obtain performance gain over existing RPL....
Traffic flow optimization and trajectory guidance in merging zones have significant implications for improving capacity and reducing time consumption. The development of V2X communication provides new insights to solve this problem by tackling the information and releasing trajectories schemes. Therefore, this paper aims to discuss the trajectory management in the merging zone for ACC vehicles. A vehicle dispatching and car-following model is proposed to generate steady traffic flow first. An algorithm framework for consecutive traffic flow is presented with the idea of FIFO rules. Then, a two-step method for an individual vehicle is discussed in detail to compute a trajectory. The first step is to select and determine the priority of the optional gaps. The next step is to verify the options’ feasibility, decide on the target gap, and output the trajectories to merge successfully. Numerical experiments validate that the proposed method guarantees safe driving and provides relatively smooth trajectories to the vehicles. Furthermore, increased capacity and higher velocity are observed in a comparative experiment. The cooperative optimization algorithm could be applied efficiently in practice and benefit from its rapid response and low computation complexity....
Analysing shortest path for real time traffic environment is crucial with dynamic updating. VANET technology can be used for\nanalysing traffic but generates a huge amount of data to be exchanged, which demands more processing power and resources.\nIn this paper, a new histogram-based route guidance algorithm (HBA) has been proposed based on light weight processing. The\nproposed algorithm enables selecting the shortest path between any source and destination using the histogram models, which\ncapture the higher order distribution function of the number vehicles in every lane. Furthermore, the histogram model is used to\nestimate the traffic delays at intersections and roundabouts.Thedata entity collection through sensors used for histogrammodelling\nis presented in detail.The experimental results show that the proposed algorithm provides a good prediction of road traffic status\nand a better solution for the congestion problem in the urban areas....
In this paper, a real-time online data-driven adaptive method is developed to\ndeal with uncertainties such as high nonlinearity, strong coupling, parameter\nperturbation and external disturbances in attitude control of fixed-wing unmanned\naerial vehicles (UAVs). Firstly, a model-free adaptive control (MFAC)\nmethod requiring only input/output (I/O) data and no model information is\nadopted for control scheme design of angular velocity subsystem which contains\nall model information and up-mentioned uncertainties. Secondly, the\ninternal model control (IMC) method featured with less tuning parameters\nand convenient tuning process is adopted for control scheme design of the\ncertain Euler angle subsystem. Simulation results show that, the method developed\nis obviously superior to the cascade PID (CPID) method and the\nnonlinear dynamic inversion (NDI) method....
Driver face monitoring system is a real-time system that can detect driver fatigue and distraction using machine vision approaches. In this paper, a new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions. In this method, face template matching and horizontal projection of top-half segment of face image are used to extract hypovigilance symptoms from face and eye, respectively. Head rotation is a symptom to detect distraction that is extracted from face region. The extracted symptoms from eye region are (1) percentage of eye closure, (2) eyelid distance changes with respect to the normal eyelid distance, and (3) eye closure rate. The first and second symptoms related to eye region are used for fatigue detection; the last one is used for distraction detection. In the proposed system, a fuzzy expert system combines the symptoms to estimate level of driver hypo-vigilance. There are three main contributions in the introduced method: (1) simple and efficient head rotation detection based on face template matching, (2) adaptive symptom extraction from eye region without explicit eye detection, and (3) normalizing and personalizing the extracted symptoms using a short training phase. These three contributions lead to develop an adaptive driver eye/face monitoring. Experiments show that the proposed system is relatively efficient for estimating the driver fatigue and distraction....
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