Vehicle Users Research Articles

Two-Layer Cooperative Optimization of Flexible Interconnected Distribution Networks Considering Electric Vehicle User Satisfaction Degree

The scaled access of electric vehicles (EVs) exacerbates load fluctuations in distribution networks, which is not conducive to the stable and economic operation of the distribution networks. At present, user satisfaction degree is generally low. To avoid this problem, this paper proposed a two-layer cooperative optimization of flexible interconnected distribution networks considering EV user satisfaction degree. First, the EV user satisfaction degree model is established by considering EV users’ charging waiting time, charging power, and other indicators. At the same time, an EV charging mode switching model is constructed based on the number of EVs entering the network and their battery charge state. On this basis, the Monte Carlo algorithm is used to generate the results of the daily distribution of EV loads taking into account the user satisfaction degree, so as to improve the load ratio of the transformer in the distribution network. Further, a two-layer cooperative optimization of flexible interconnected distribution networks considering electric vehicle user satisfaction degree is developed with the daily operating cost of each network as the optimization objective. Finally, a flexible interconnected power distribution network consisting of three power distribution networks is used as an example for validation. The results show that this method is effective in improving EV user satisfaction degree and reducing the peak-to-valley ratio of the system load while taking into account the safe and economic operation of the distribution network, which greatly improves the reliability and economy of the operation of the flexible interconnected power distribution network.

Remote Instantaneous Power Consumption Estimation of Electric Vehicles from Satellite Information

Instantaneous Power Consumption (IPC) is relevant for understanding the autonomy and efficient energy usage of electric vehicles (EVs). However, effective vehicle management requires prior knowledge of whether they can complete a trajectory, necessitating an estimation of IPC consumption along it. This paper proposes an IPC estimation method for an EV based on satellite information. The methodology involves geolocation and georeferencing of the study area, trajectory planning, extracting altitude characteristics from the map to create an altitude profile, collecting terrain features, and ultimately calculating IPC. The most accurate estimation was achieved on clay terrain with a 5.43% error compared to measures. For pavement and gravel terrains, 19.19% and 102.02% errors were obtained, respectively. This methodology provides IPC estimation on three different terrains using satellite information, which is corroborated with field experiments. This showcases its potential for EV management in industrial contexts.

Implementation of the European Union Legal Framework for Electric Vehicles in Montenegro

Transitioning to electric drive and using energy from renewable sources can help in the decarbonisation process globally. Wider usage of electric vehicles and charging infrastructure development depend on the legislative and regulatory framework. In this paper, we have analyzed the legal and regulatory framework for electromobility in the European Union. This analysis is conducted to propose adequate measures which should be implemented in Montenegro to accelerate electric vehicle adoption. Proposed measures must be in accordance with the current legislative and regulatory framework in the European Union. Additionally, a brief overview of current state in this area in Southeast Europe countries is conducted. We would like to initiate changes in Southeast European countries to make progress in this area.

Experimental study on efficient propulsion system for multicopter UAV design applications

The usage of multicopter unmanned aerial vehicles (UAVs) has increased for various military and civilian purposes. The choice of propulsion system of such a vehicle is crucial to fulfill the intended mission requirements. The present study focuses on evaluating the efficiency of propulsion system by experimenting with different motor, propeller and battery combinations. The connection between the electronic speed controller (ESC) signal, current, power, thrust and torque in relation to propeller size is determined. It is observed that regardless of battery capacity or motor type, the thrust and torque produced for a given motor speed (RPM) for a specified propeller are similar. The higher capacity battery with 6000 mAh, denoted as B2 battery, consumes less current and can attain higher motor speed to produce the required thrust force than a lower capacity battery with 3300 mAh, denoted as B1 battery. The most efficient propeller, 12 inches in diameter (P4 propeller), is observed to achieve efficiency levels of 12.9 % for the B1 battery and 11.4 % for the B2 battery. Similarly, the most efficient motor, 700 KV motor (M1), is determined to exhibit efficiency of 64.29 % when coupled with the B1 battery and 62.01 % when coupled with the B2 battery. It is identified that using the B2 battery results in an increased payload capacity of 5.82 N, compared to 2.02 N with the B1 battery. Furthermore, when considering both scenarios with and without payload, greater endurance is observed when B2 battery is used as opposed to the B1 battery.

Promoting Veteran-Centric Transportation Options through Exposure to Autonomous Shuttles

Veterans face difficulties accessing vital health and community services, especially in rural areas. Autonomous vehicles (AVs) can revolutionize transportation by enhancing access, safety, and efficiency. Yet, there is limited knowledge about how Veterans perceive AVs. This study fills this gap by assessing Veterans’ AV perceptions before and after exposure to an autonomous shuttle (AS). Using a multi-method approach, 23 participants completed pre- and post-AS Autonomous Vehicle User Perception Survey (AVUPS), with 10 participants also taking part in post-AS focus groups. Following exposure to the AS, differences were observed for three out of the four AVUPS domains: an increase in Intention to Use (p < 0.01), a decrease in Perceived Barriers (p < 0.05), and an increase in Total Acceptance (p = 0.01); Well-being remained unchanged (p = 0.81). Feedback from focus groups uncovered six qualitative themes: Perceived Benefits (n = 70), Safety (n = 66), Shuttle Experience (n = 47), AV Adoption (n = 44), Experience with AVs (n = 17), and Perception Change (n = 10). This study underscores AVs’ potential to alleviate transportation challenges faced by Veterans, contributing to more inclusive transportation solutions. The research offers insights for future policies and interventions aimed at integrating AV technology into the transportation system, particularly for mobility-vulnerable Veterans in rural and urban settings.

Distributionally Robust Model Predictive Control for Smart Electric Vehicle Charging Station With V2G/V2V Capability

This paper proposes a distributionally robust model predictive control (DRMPC) for energy management of a vehicle-to-grid (V2G)/vehicle-to-vehicle (V2V)-enabled smart electric vehicle charging station (EVCS) with a photovoltaic (PV) system and an energy storage system. The proposed DRMPC method aims to reduce the operational cost of the EVCS while ensuring the desired charging demands of electric vehicle (EV) users under uncertainties in electricity buying/selling prices, PV generation outputs, and future EV charging demands. To cope with these uncertainties, the proposed method includes the following three features: i) tractable reformulation of the worst-case expected buying cost and selling revenue using a Wasserstein metric and duality theory, ii) determination of a distributionally robust bound on the random PV generation output using its support information, and iii) a scenario-based approach to predicting the future EV charging demand. To improve computational efficiency, a penalty method is proposed to relax the complementarity constraints, while still ensuring nonsimultaneous charging and discharging of EVs under the derived sufficient conditions. Numerical examples using a real-world operational dataset of the EVCS are provided to demonstrate the effectiveness of the proposed DRMPC method under uncertain environments in terms of the EVCS cost saving via V2G/V2V capability, data utilization, and computational complexity.

Optimal routing for electric vehicles in hybrid charging networks

Charge-while-driving technology is a promising application in the future. A routing approach suitable for hybrid stationary and dynamic wireless charging networks is thus worth investigating. This paper aims to determine the optimal path with minimum generalized travel cost as well as provides charging policy recommendations for electric vehicle (EV) users. A hybrid charging network, including charging stations and wireless lanes, is constructed first. The generalized travel cost is then investigated to help EV users understand the complicated cost components. A dynamic programming algorithm is developed as the solution measure. Numerical experiments show that a higher level of wireless charging lane penetration can significantly reduce generalized travel costs, especially implicit costs such as travel time cost or stopping cost. EVs are more likely to prefer wireless charging modes when the value of the user's time and the cost of stopping is high. The methodology proposed in this study not only provides services to EV owners, such as navigation, but is also a useful tool for administrations wishing to direct incentives to facilitate the transition to more sustainable energy sources, as it quantifies the benefits of wireless charging for different network attributes.

Vehicular social aspects in data dissemination of urban critical events with QoS and QoE for supporting emergency response

AbstractCritical urban events impact the city agenda, like sustainable urban mobility, safe transportation, and efficient governance, thus leading many times to social disturbance, environmental degradation, and economic and human losses. Such events demand efficient dissemination of their data to the authorities to take rapid and effective actions to minimize their effects. The Social Internet of Vehicles (SIoV) paradigm can aid in monitoring such events in time and space. Vehicles are suitable for monitoring random events in space and time due to their ubiquity. Simultaneously, social relationships between vehicle users establish local communities where social intelligence can help disseminate data collected by VANET to support public management in event recognition. Thus, this article proposes a distributed and robust QoS/QoE aware, social‐based data dissemination solution for critical urban events and investigates how social parameters influence the quality of data dissemination. The impact of social influence, social activity, and friendship strategies was analyzed by simulation. The results show the gains in QoS when using social activity and in QoE subjective metrics when employing social influence.

Do car drivers respond differently to fuel price changes? Evidence from German household data

AbstractFor cutting down greenhouse gas emissions in road transport, core economic measures are relying on the response to increasing fuel prices—i.e., the fuel price elasticity. A focal point of the effectiveness of these measures is the heterogeneity in fuel price elasticities of different vehicle users. These effects were often neglected in the transport-related literature and were only incorporated recently. The results show, however, sometimes contradicting conclusions on influencing parameters such as income, region-type or household size. In this paper, we used a pooled OLS model estimated on a German refuelling diary data set and analysed the impact of various household level characteristics on fuel price elasticities through an analysis of interaction terms and their marginal effects. This analysis provides a cornerstone in this discussion on fuel price elasticities. We found out that the overall results contrast the existing literature by identifying heterogeneity in fuel price elasticities among German households for different socio-economic and regional characteristics. The results are highly relevant for policy modellers and for introducing effective policy measures for mitigating greenhouse gas emissions in road transport.

Mathematical Modeling of a Fuzzy Logic-Based Car Parking System

This paper presents the mathematical modeling of a fuzzy logic-based car parking system. Many people are facing problems parking vehicles in parking lots in most urban cities. The technology of fuzzy-based systems is necessary for all vehicle users to acquire parking slots in cities. A smart parking system helps in obtaining information about available parking spaces, organizing and processing it, and then placing the car in a certain position. The method adopted makes use of a fuzzy-based smart car parking system to develop the usage of small parking spaces for parking lots to allow the parking of vehicles based on their weight. A set of rules was used in the fuzzy logic to control two sets of parking decisions, namely the vehicle entry decision and the inpark decision, by bearing in mind the weight of vehicles and the availability of parking spaces in the parking mall. The results obtained show that the smart car parking system keeps the availability of parking lots in equilibrium, depending on the entry and exit of vehicles. In conclusion, it was discovered that the total number of vehicles granted entry at each time interval depends on the availability of parking lots on each designated floor.

DECISION TREE PADA PENILAIAN PERFORMA LAYANAN DRIVER GOJEK DENGAN ALGORITMA C4.5

Transportation is an important means to support daily activities and operations, be it private transportation or public transportation. This study will analyze the level of customer satisfaction as a public vehicle user, in this case the author utilizes data on customers who use and utilize the gojek application as a means of transportation, using a questionnaire data collection technique, the writer will determine client satisfaction levels using the variables Tangible, Reliability, Responsiveness, Assurance, Empathy. The analytical Data mining with the is a technique employed in this investigation. concept of the Algorithm C4.5 classification which can show the results of the highest Entrophy and gain values modeled as a decision tree with the aim that the results obtained can be a guide for gojek partners to service and increase customers. Considering the outcomes of the classicization It is demonstrated by the C4.5 algorithm that the accuracy achieves 75.33% where the factors that become the assessment of satisfaction are Tangible, Reliability.

A Framework of Vehicle Usage Optimization for Tour Purposes

Nowadays, people like to travel to alleviate the stress and depression they experience in their busy lives, and if they can do so at a low cost, it will be even more beneficial to them. A traveler can choose to book a travel ticket themselves or to contact a travel agency, who will book a ticket for them. A lack of vehicle management can cause travel agencies to lose profit. In this article, we have provided proficient solutions for vehicle optimization to minimize travel costs. Additionally, however, arranging an appropriate tour guide is sometimes expensive for travelers. Moreover, travelers often do not know about suitable travel packages and occasionally do not know where they may want to visit. In this paper, we have discussed how we used effective methods to reduce vehicle costs for tour purposes. Furthermore, an optimal bus seat occupancy solution has been developed, incorporating the package system and including a tour guide. Our system offers a remedy by providing access to cost-effective packages. We have also added a blog portal to our web-based software for an overview all tourist places and their information. As a result, we have found optimized routes and provide low-cost, high-value tours. We have also reduced carbon footprint emissions. Our proposed optimal vehicle usage system has been developed using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), the Tailwind Framework, React, and Node.js.

Design And Implementation of An Efficient Hybrid Two-Wheeler Vehicles

Nowadays the population of people increases in high volume. Due to that transportation plays a major role to move one place to another place. So the need for vehicle is also high, there has been increase in usage of Oil and Gas vehicle leading to problems like Global Warming, climate change, shortage of fossil fuel, etc. Due to these reasons Automobile Companies have started doing research for making Hybrid Technology usable into the day to day life. So the Automobile companies have started producing the Electronic-Bike to overcome the above problems. There is a lack of rechargeable point in outer area ,this may arise unwanted delay as well as create lot of problems. To overcome these problems, we proposed a new methodology to develop a new hybrid vehicle which runs with the help of both Fuel and Electricity .This Paper is mainly concentrate on the survey on existing methodology ,explanation about the proposed technologies, their function, advantages, efficiency of the proposed system, and the fuels and raw materials used in the Hybrid Vehicle. Finally we conclude the paper with the advantages and disadvantages of Hybrid vehicle and discuss how this technology will take over the world in future .

Sistem Pemantauan Slot Parkir Menggunakan Sensor Ultrasonik JSN-SR04T dan Pengenalan Plat Nomor Kendaraan dengan ESP32-CAM

A parking slot monitoring system prototype has been produced using the JSN-SR04T ultrasonic sensor and vehicle license plate recognition with ESP32-CAM. The system was created to make it easier for motorists to monitor the availability of parking slots remotely by utilizing Telegram which is connected to the ESP32-CAM module and Arduino Mega 2560. The ultrasonic sensor detects the distance of vehicles entering or exiting the parking lot, then a servo motor opens the entry and exit bars. Vehicle users can monitor the availability of parking slots on the Telegram bot. In each slot there is an ultrasonic sensor that detects the distance so that it can find out whether there is a vehicle in the slot. The test results show that the ultrasonic sensor has an average error percentage of 0.65%. The parking barrier can be opened when the ultrasonic sensor detects the presence of a vehicle at the entrance or exit and the ultrasonic sensor can detect the presence of a vehicle according to the conditions in the slot. The system is equipped with vehicle license plate character recognition using the Optical Character Recognition (OCR) method. Through the ESP32-CAM module camera webserver, the OCR program can detect vehicle plates with the furthest distance of 140 cm using video resolution (400x296) and 300 cm using video resolution (1600x1200). The OCR program can read the vehicle plate characters correctly and the results for the number of characters obtained correspond to the license plate being tested, then the vehicle plate data is stored in the database

A Blockchain-Powered Traffic Management System for Unmanned Aerial Vehicles

The increasing popularity and usage of unmanned aerial vehicles (UAVs) has brought about new challenges in airspace management. With the number of drones expected to grow even further in the coming years, there is an urgent need for an autonomous traffic management system (TMS) that can safely and effectively manage drone traffic in the airspace. It is critical that this TMS be built with principles of the Confidentiality, Integrity, and Availability (CIA) triad. In this paper, a traffic management system for UAVs is presented that takes advantage of a Hyperledger Fabric blockchain network. The TMS provides a decentralized and secure method to manage and deconflict drone flight paths, allowing for safe navigation in crowded airspaces. Through a series of simulated experiments, we demonstrated the system’s capabilities in handling path creation, multiple conflict resolutions, and large numbers of drones. Simulated tests showed that the proposed system was able to handle deconfliction of 1000 drones inside of a one square kilometer, and returned calculated paths for drones in 60 to 2000 ms with up to 100 deconflictions. The Hyperledger Fabric powered traffic management system showcased the potential to leverage permissioned blockchain technology in improving drone traffic management.

Switching mode allocation in planning paths for vehicular network communication

Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.

Dynamic Resource Allocation in Queue-Constrained and Delay-Sensitive Vehicular Networks

Since performing reliable and real-time data transmission is challenging, this paper focuses on the dynamic resource allocation framework with queue and delay constraints in vehicular relay networks, where vehicle users and roadside users are in cooperative communications. To reduce data fluctuation and tackle channel uncertainty, the Lyapunov optimization theory is integrated into the long-term dynamic resource allocation framework, which addresses multiple performance indicators including queue backlog, communication delay, and user utility. In particular, the hierarchical competitive relationship between vehicle users and roadside users is captured, and the Stackelberg game structure is adopted to maximize the utilities of all users. Furthermore, the lower-game subproblem is transformed by the Lyapunov optimization method and is solved by the Lagrangian function method. As the best response function, the lower-game solution is used to formulate the upper-game strategy. Since the existing of game equilibrium (GE) solution, a Stackelberg game-enabled robust resource allocation algorithm can be proposed. Finally, various numerical simulations are performed to show that the proposed algorithm has better robustness and higher throughout than the benchmarks.

On the design of trust and mobility based evaluation for intelligent collaborative UAVs assisted VANETs

In recent days, vehicles usage and speed are highly increased that leads to an increase in energy consumption, delay, and overhead in the network. In this paper, a novel trajectory is introduced to achieve maximum reliability namely trust and mobility-based evaluation for intelligent collaborative (TMIC)-UAVs assisted VANETs. Reactive multipath greedy routing protocol (RMGR) is the hybrid routing protocol and it is the combination of ad hoc on-demand multipath distance vector (AOMDV) with greedy geographic forwarding (GGF) which is used for routing in frequently changeable network topology. To protect the network from malfunctions, effective trust evaluation (ETE) is performed by calculating the direct trust and indirect trust. Finally, to achieve effective communication among the UAVs, hybrid optimization is performed which is the combination of the genetic algorithm (GA) and the crow swarm optimization (CSO) algorithm. For validation network simulator (NS3) is used and the results show that this approach achieves high energy efficiency, delivery ratio, and reduction in delay when compared with the earlier research.

PHY Security Design for Mobile Crowd Computing in ICV Networks Based on Multi-Agent Reinforcement Learning

In this paper, we propose a multi-roadside unit (RSU) assisted mobile crowd computing framework for intelligently connected vehicle (ICV) networks, where vehicles within RSUs’ coverage act as workers to provide their computation and communication resources for computing resource limited vehicle user equipments (VUEs). Physical (PHY) layer security is used to secure computation task offloading and results feedback in time-varying vehicular channels. Artificial noise (AN) assisted adaptive wiretap coding is adopted to enhance the security of offloading links. With PHY security, the intended receiver can decode secret message while eavesdropper cannot. A modified exhaustive two-dimensional (2D) search algorithm is proposed to optimize transmission rate and secrecy rate in an effective secrecy throughput maximization problem, and a multi-agent twin delayed deep deterministic policy gradient algorithm (MATD3) is utilized to assign VUEs’ tasks without a central controller, where a reward function is defined according to the computing costs, including execution time, energy consumption, and price paid for computing. Finally, simulations verify the effectiveness of the proposed framework.

A Review on the Performance of Aggregator of EV for Power Market

Abstract: This paper presents a review on the performance of aggregator of EV for power market. The EV aggregator is defined as an interface entity that aggregates the EVs to act as dispersed energy resources, enabling them to support the V2G mechanism. There are various methods to optimize the performance of electric vehical aggregator for power market. This paper analyzes the bidding strategy problem of an aggregator managing a set of electric vehicles. The electric vehicle aggregator determines the bidding decisions in the market that minimize the charging costs of electric vehicles and, at the same time, meets the driving requirements of electric vehicle users. It will take into account the uncertainty in both market prices and driving needs using a set of scenarios, which allows formulating the problem using a stochastic programming approach. The impact of the bidding decisions of the electric vehicle aggregator on market prices is also addressed in the decision-making tool. A number of illustrative examples explain how to formulate and solve this type of problems.