Vehicle Users Research Articles

Optimizing smart microgrid performance: Integrating solar generation and static VAR compensator for EV charging impact, emphasizing SCOPE index

The rapid global increase in electric vehicle (EV) usage, driven by its low CO2 emissions, uncomplicated maintenance, and minimal operating costs, has prompted extensive research in the field of electric vehicle charging station (EVCS). The integration of EVCS into the current distribution grid poses challenges due to potential power losses and voltage variations beyond acceptable limits. This complexity is heightened by the growing penetration of randomly dispersed solar-based distributed generation (SDG) and battery energy storage system (BESS). To address these challenges, distribution static VAR compensator (DSVC) systems have been introduced, offering benefits such as enhanced power transfer capacity, improved voltage regulation, and increased system security without requiring extensive infrastructure upgrades. This study offers SCOPE, a novel multi-objective framework that unifies the optimization goals of minimizing real power loss, lowering bus voltage variation, maximizing system voltage stability, minimizing system operating costs, and mitigating CO2 emissions. The EVCS problem is optimized within this multi-objective framework utilizing an improved bald eagle search algorithm (IBESA). The proposed model accounts for vehicle to grid (V2G) capabilities and the actual driving patterns of users over a 24-h horizon. The formulation of a smart microgrid (SMG) structure is based on modifying the standard IEEE 33-bus test radial distribution network (RDN), comprising three interconnected SMGs serving residential, commercial, and industrial users. The optimization approach based on IBESA is utilized to optimize both the siting and capacity of EVCS as well as renewable energy sources (RESs). The findings show that SDG and DSVC are effective at lowering the SCOPE index, highlighting the advantages of the suggested approach.

Rush to Charge, Dead to Drive: Application of Deadline Rush Model to Electric Vehicle User's Charging Behavior.

This work aims to estimate the portion of electric vehicle (EV) users who exhibit procrastination-like behavior, almost equivalent to an "empty" battery, before they decide to charge their vehicles. There is a human tendency to procrastinate when a deadline approaches. Human behavior in the presence of deadlines has been studied in different fields to evaluate individuals' performance or organizational efficiency and effectiveness. However, this phenomenon has not been investigated among EV users. This study explores users' procrastination-like behavior among 69 Rhode Island public charging stations' data representing 70,611 charging events. The Deadline Rush Model is incorporated to model frequent users' charging profiles. To conduct a robust estimation, the Bayesian Mixture Model is implemented. With the selection of an informative prior, the Bayesian Mixture Model estimated that almost one-third of frequent users procrastinate charging. The majority of procrastination-like users have small battery sizes. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid; thus, systematically, they can clog the system for other users whose needs are more pressing. Understanding unique and unexplored charging behaviors among EV users is beneficial to EV infrastructure stakeholders in reducing the adoption threshold by providing a reliable and ubiquitous charging network. The findings identify a different kind of demand on the EV infrastructure than previously modeled and can directly influence future decision-making criteria in terms of planning to optimize to accommodate EV drivers with different charging behaviors.

Short-Term Shoreline Evolution Mapping by using UAV

The boundary separating land from the beachside is known as the coastline. Monitoring and analysing shorelines are crucial because it gives us a better understanding of shoreline changes and identifies beach conditions. Unmanned aerial vehicle (UAV) or drone usage has emerged as a beneficial technology in coastal engineering, notably in the study of shoreline evolutions, because it is more credible and more straightforward to use than the prior technologies. This study aims to gather coastal data, evaluate Pantai Punggur shorelines, and track shoreline changes using processed imagery. The "Pix4D Capture" application has been programmed with the flight planning route, and the drone takes pictures along the entire grid line provided. The data on shoreline evolutions were processed using the Pix4d Mapper and Global Mapper tools. Shoreline changes were tracked continuously from August 4th, 2020, through August 10th, 2021. According to the results, Pantai Punggur is undergoing erosion, as evidenced by the alterations in the shoreline that were apparent in less than a year.

SPAGHETTI: a synthetic data generator for post-Covid electric vehicle usage

The Covid-19 pandemic has resulted in a permanent shift in individuals’ daily routines and driving behaviours, leading to an increase in remote work. There has also been an independent and parallel rise in the adoption of solar photovoltaic (PV) panels, electrical storage systems, and electric vehicles (EVs). With remote work, EVs are spending longer periods at home. This offers a chance to reduce EV charging demands on the grid by directly charging EV batteries with solar energy during daylight. Additionally, if bidirectional charging is supported, EVs can serve as a backup energy source day and night. Such an approach fundamentally alters domestic load profiles and boosts the profitability of residential power systems. However, the lack of publicly available post-Covid EV usage datasets has made it difficult to study the impact of recent commuting patterns shifts on EV charging. This paper, therefore, presents SPAGHETTI (Synthetic Patterns & Activity Generator for Home-Energy & Tomorrow’s Transportation Investigation), a tool that can be used for the synthetic generation of realistic EV drive cycles. It takes as input EV user commuting patterns, allowing for personalised modeling of EV usage. It is based on a thorough literature survey on post-Covid work-from-home (WFH) patterns. SPAGHETTI can be used by the scientific community to conduct further research on the large-scale adoption of EVs and their integration into domestic microgrids. As an example of its utility, we study the dependence of EV charge state and EV charging distributions on the degree of working from home and find that there is, indeed, a significant impact of WFH patterns on these critical parameters.

First mile and last mile analysis using users’ perceived quality of Jakarta Mass Rapid Transit (MRT Jakarta) North-South Corridor Phase 1

To address urban problems caused by high usage of private vehicles, it is important to understand how MRT Jakarta as a new public transportation mode in Jakarta, could support the mobility needs of its users not only within MRT stations but also throughout the entire journey from origin to destination. This study aims to analyze the socio-demographic characteristics of MRT Jakarta users, the first mile and last mile spatial mobility pattern, and the perceived quality of overall journey using MRT Jakarta. An online questionnaire was employed to collect data directly from MRT Jakarta users, encompassing socio-demographic information, spatial mobility patterns, and users’ evaluations on several influencing factors. The study concludes that improving the quality of the first mile segments can significantly improve the overall journey quality using MRT Jakarta.

Simulation and Fabrication of Hybrid Electric Bicycle with Multi Charging Mode

The use of electric vehicles actively contributes to environmental conservation. Lack of EV charging infrastructure is one of the most common challenges associated with the electric vehicles. The conventional method for the electric propulsion system deals with the single mode of charging techniques. The alternative battery charging techniques helps to enhance the usage of electric vehicles. In this work hybrid electric bicycle is designed with multi charging method. In addition to the conventional wall charger, a solar panel and dynamo is used to charge the battery. Increasing the performance and efficiency by means of BLDC motor. The entire propulsion system is connected with the PI controller which calculate the speed and relevant voltage needed for the drive. The performance of the proposed method is evaluated through simulation using MATLAB/ Simulink software.

Multilayered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. Lastly, case studies and a comparative analysis prove the efficacy of the proposed framework.

CHALLENGES AND BARRIERS FOR UNMANNED AERIAL VEHICLE (UAV) IMPLEMENTATION IN MALAYSIAN INFRASTRUCTURE PROJECTS

The usage of Unmanned Aerial Vehicles (UAV) in infrastructure projects has inclined drastic recently due to its benefits. However, there is a slowness in transition from conventional construction approach to new innovation. Besides, there are lots of issues in current practices such as cost overrun, dangerous, delay of time completion and lack of an overall comprehensive scientific system. Furthermore, although there was high level of awareness on the advantages of using UAVs, the Malaysian construction industry are still reluctant for the changes. In response, to overcome issues that the construction industry faces, such as infrastructure projects thus, this study aims to develop a conceptual framework for promoting the applications of UAV in Malaysia infrastructure projects. The methods adopted include explanatory sequential research methods. For instance, questionnaire surveys and interviews are used to collect quantitative and qualitative data, respectively. Besides that, a refined conceptual framework of the challenges and barriers of UAV adoption in Malaysia infrastructure projects were mainly contributed by the restrictive regulatory environment and industry point of view. For example, the privacy issues and guidelines on autonomous UAV and the industry lack of confidence in the adoption of UAV and education on the adoption of the UAV. In addition, it offers some solutions for the issues that have been revealed in the preliminary conceptual framework for the challenges on UAV adoption in Malaysia infrastructure projects. For instance, amaze the contractor with the final data from the UAV. Lastly, the identify solution served as a reference for other Malaysian construction stakeholders that have not used UAVs before. The recommendations for future scholarly research include the roles of UAV technology with other technologies such as Artificial Intelligent (AI) or integration of data with other software.

Optimizing Cold Chain Distribution Routes Considering Dynamic Demand: A Low-Emission Perspective

Cold chain logistics, with its high carbon emissions and energy consumption, contradicts the current advocacy for a “low-carbon economy”. Additionally, in the real delivery process, customers often generate dynamic demand, which has the characteristic of being sudden. Therefore, to help cold chain distribution companies achieve energy-saving and emission-reduction goals while also being able to respond quickly to customer needs, this article starts from a low-carbon perspective and constructs a two-stage vehicle distribution route optimization model that minimizes transportation costs and refrigeration costs, alongside carbon emissions costs. This research serves to minimize the above-mentioned costs while also ensuring a quick response to customer demands and achieving the goals of energy conservation and emission reduction. During the static stage, in order to determine the vehicle distribution scheme, an enhanced genetic algorithm is adopted. During the dynamic optimization stage, a strategy of updating key time points is employed to address the dynamic demand from customers. By comparing the dynamic optimization strategy with the strategy of dispatching additional vehicles, it is demonstrated that the presented model is capable of achieving an overall cost reduction of approximately 17.13%. Notably, carbon emission costs can be reduced by around 17.11%. This demonstrates that the dynamic optimization strategy effectively reduces the usage of distribution vehicles and lowers distribution costs.

Public Awareness and Behavioral Patterns During Smog

Smog is also an alarming signal for health problems in Pakistan especially in the city Lahore. Objective: To find out how the general public feels about smog. Methods: The study was conducted by surveying Lahore residents who drive. This was a survey-based study which included 100 subjects using any vehicle as a mode of transportation. Results: Generally, 2% people used cycle, motorbike 62%, car 13% and public transport 23% but during smog people shifted from bike to public transport and rate of car users were also increased from 13% to 25%. In this survey, 58% of the people came to know about smog through TV channels. Most of the respondents had the symptoms of chest tightness, eye irritation, breathing difficulty. This study revealed that 85% of vehicle users took preventive measures such as kept themselves hydrated (18%), worn mask (58%) and driven less (9%); which kept them away from hospitals during smog. Conclusions: It was anticipated that motor bike, car and public transport users had more knowledge about the causes of smog but none of cycle user was aware that sulfur and nitrogen compounds were the cause of smog (p<0.001). Thus, Pakistan needs to implement future countermeasures for smog.

Edge computing‐based optimal dispatching of charging loads considering dynamic hosting capacity

AbstractOwing to the rapid increase in electric vehicle integration and the uncoordinated charging behaviour of electric vehicles, the overloading risk of distribution transformers has deteriorated. This impact caused by large‐scale electric vehicle integration can be effectively reduced through the orderly guidance of electric vehicle charging behaviours. Here, a dispatching strategy for charging loads is proposed to address the problems of the uncoordinated charging demand in electric vehicles and overloading risk of distribution transformers in residential areas. First, an edge‐side dynamic index of the electric vehicle hosting capacity is proposed to guide the optimal dispatching of charging loads. Subsequently, an optimal dispatching model of the charging loads is established based on edge computing. The edge‐side dispatching strategy for the charging loads is then further updated considering the participation willingness of electric vehicle users. Finally, the effectiveness of the proposed control strategy is validated using a modified residential distribution network in Tianjin. The results show that the proposed strategy can effectively decrease the overloading risk of distribution transformers while realizing the efficient operation of electric vehicles on the edge side.

Travel Satisfaction of Bus Rapid Transit Users in A Developing Country: The Case of Bhopal City, India

India's burgeoning population has led to an increased demand for transportation services, particularly in urban areas. To address the growing mobility needs and mitigate the adverse effects of personal vehicle usage, efficient public transit services are imperative. Several Indian cities have implemented bus rapid transit (BRT) systems to address this problem, but ridership for the majority of the BRT systems remains below breakeven (this refers to the point at which the revenue generated from ridership equals or surpasses the costs associated with operating and maintaining the transportation system). Thus, it is necessary to assess the determinants of commuter satisfaction with BRT. The present study aims to examine factors affecting travel satisfaction among BRT commuters. By using structural equation modeling (SEM), the effect of demographic (age and gender) and travel-related variables (fare price, travel time, trip frequency, reliability, ease of using bus service, and comfort level) on BRT commuters' satisfaction and usage of BRT services were analyzed. It was found that while ease of using the service, comfort level, and reliability positively affect BRT travel satisfaction, trip frequency is significantly affected by the ease of using the service only. Furthermore, demographic variables (age, and gender of respondents) were not found to have significant effect of travel satisfaction and trip frequency. The findings from this can study serve as a base for policymakers, city officials, and urban planners to identify commuters' priorities for a well-functioning BRT in India and formulate policies to attract riders by enhancing their preferences.

Joint Incentive Mechanism Design and Energy-Efficient Resource Allocation for Federated Learning in UAV-Assisted Internet of Vehicles

With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs’ intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs’ contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%.

Quantitative risk assessment for static and mobile road users: methodology and application at A82 Glen Coe, Scotland

BackgroundIn August 2004 a series of debris flows caused significant disruption to the Scottish (strategic) Trunk Road Network. The subsequent Scottish Road Network Landslides Study identified a number of sites considered to be at highest risk. Some of these sites have been the subject of formal quantitative assessment of the risk from debris flow to mobile road users in vehicles. The A82 in Glen Coe has the added complication that two car parks have developed on debris fans exposing significant numbers of people to the risk while, they are essentially static and largely outside their vehicles.MethodologyThe risk to road users is determined using a previously developed probabilistic methodology for mobile road users (mobile elements at risk) and a new and related methodology developed for static road users (static elements at risk) is described and applied. Within the latter, an entirely new metric of Annual Average Daily Visits is used to allow the temporal component of the probability of a landslide impacting a person to be determined given the occurrence of an event.ResultsWhile Personal Individual Risk is at an acceptable level, including for frequent users, the risk presented to society as a whole presents a rather different picture; this is largely due to the number of visitors. The results assess the overall, societal risk for mobile elements at risk as As Low As reasonably Practicable, being at a similar level to other sites, albeit with a higher risk associated with higher numbers of fatalities. The results for the static elements at risk on the other hand suggest that the risks are classified as Unacceptable for higher numbers of fatalities. The assessment of the total societal risk, for mobile and static elements at risk, at the A82 Glen Coe suggests As Low As Reasonably Practicable for low numbers of fatalities but classify as Unacceptable for higher numbers of fatalities (around 20 to 250).

Analysis of EU Countries’ Capabilities to Achieve Ambitious “Zero Victim” Vision in 2050

The objective of this research paper is to examine and assess the correlation between the economic expansion of European Union member states and the escalating rate of motorization, which subsequently leads to road accidents resulting in significant loss of life and injury. A statistical analysis of economic growth, the rate of motorization, and road incidents in EU member states from 2010 to 2020 constitutes the research methodology. The research paper employs a quantitative approach to analysis and comparison. The research paper demonstrates how an increase in the number of motor vehicles in EU member states contributes to road accidents and human mortality. Varying degrees of economic development are associated with disparities between the rate of motorization expansion and the decline in fatalities. Because the rate of increase in motor vehicle usage surpasses the decline in fatalities per motor vehicle at low-income levels. At increased levels of income, the opposite occurs. Practical Implications: This research paper illustrates the importance for road traffic safety authorities to be aware of safety performance indicators and to incorporate them into legislative deliberations in order to bolster the European Union’s “zero victims” objective and provide enhanced protection for motor vehicle accident victims. Originality – This paper examines the correlation between motorization rates and fatalities in various EU member states with varying degrees of economic development over the past few decades.

Coupling agent-based modelling and life cycle assessment for a behaviour-driven evaluation of SAEVs

ABSTRACT Shared Automated Electric Vehicles (SAEVs) are poised to revolutionize future transportation. However, potential drawbacks, including increased vehicle usage and the projected shorter vehicle lifespan, introduce critical factors that may impact efficiency and environmental benefits. This research introduces a framework that integrates Agent-Based Modelling (ABM) with Life Cycle Assessment (LCA) for a behaviour-driven SAEV assessment. The ABM simulates regional SAEV operations, informing the LCA of pre- and post-integration scenarios. Sensitivity analysis on fleet sizes, system performance metrics, and Global Warming Potential (GWP) reference values are performed. Findings demonstrate that SAEVs significantly decrease the fleet size and total travel distance by raising the average travel per vehicle. SAEVs integration yields a 75–86% daily GWP reduction without significantly compromising user experience. Over 30 years, fleet replacement needs due to inadequate fleet sizing raised GWP by 170%. Balancing short and long-term environmental impact requires optimizing fleet size to achieve sustainable and efficient service delivery.

Charging and Discharging Optimization of Vehicle Battery Efficiency for Minimizing Company Expenses Considering Regular User Travel Habits

With the increasing popularity and development of electric vehicles, the demand for electric vehicle charging is also constantly increasing. To meet the diverse charging needs of electric vehicle users and improve the efficiency of charging infrastructure, this study proposes an optimization strategy for electric vehicle charging and discharging. This method considers both the user’s travel mode and the operational efficiency of the charging pile. Firstly, a probability model based on travel spatiotemporal variables and Monte Carlo algorithm were used to simulate the travel trajectory of electric vehicles, providing a data foundation for optimizing the charging and discharging schemes of electric vehicles. Then, with the dual objective of minimizing the operating costs of charging piles and user charging costs, a linear programming model was constructed to optimize the charging and discharging strategies of electric vehicles. Finally, the model was validated using an apartment building as an example. The results indicate that, under the normal travel habits of users, with the goal of minimizing company expenses, the annual cost of the company reaches its minimum at a certain number of charging piles. When the cost of electric vehicle users dominates the objective function, they will pay more attention to battery degradation, significantly reducing their willingness to participate in discharge.

System for Monitoring the Safety and Movement Mechanics of Users of Bicycles and Electric Scooters in Real Conditions in the Context of Social Sustainability

Sustainable development means taking care of the environment, which also means promoting green transport, which involves the systematic development of personal transport in its broadest sense. The positive aspects associated with cheap and convenient electric transport are intertwined with the problem of collisions and accidents. While developing road infrastructure for electric vehicles such as scooters, bicycles, and others, research should be conducted in parallel to ensure the highest possible level of safety for users. There is also an increase in the number of people using bicycles and electric scooters, which develop significant speeds. The problem of accidents among users of classic and electric bicycles and scooters is evident, and post-accident injuries pose a serious challenge to medical practitioners. The literature is rich in statistical analyses of accidents among users of scooters and bicycles, but there are no studies where the behaviour of users of bicycles, scooters, etc. is analysed. The authors of this study set out to develop a measurement system to assess the traffic safety of people using bicycles and scooters. The device uses LIDAR to record the speed of the vehicle and a camera, the images of which are processed by an algorithm in order to classify the user as being on a bicycle or scooter and using or not using head protection with a helmet. It is also possible to analyse the behaviour of the vehicle users under study. The article describes the built measurement device and presents the results of the initial measurements made by the device.

Greenhouse Gasses Inventory on Textile Finishing Industry PT X

The textile industry has the biggest impact on environmental pollution in the world, and is responsible for 10% of global carbon production. Most of the waste in the textile industry, is generated from the dyeing process and textile processing. PT X is a textile finishing industry, that focuses on finishing processes like dyeing and textile processing. In this research, GHG (greenhouse gasses) emission inventory was carried out to see how much GHG generated in the textile finishing industry. The inventory will focus on CO2, CH4, and N2O from generator, curing machine, operational vehicle, and electricity usage. The inventory methodology was based on IPCC 2006, that stated to determine GHG emission, an activity data and emission factor from that source is needed. This emission inventory results on the total emissions of 666.38 tonCO2eq or 258.629 kgCO2eq/tonproduct GHG emission from PT X in 2021. The GHG emission in PT X are dominated by CO2 gas. Which most of comes from electricity usage source. GHG emission produce by PT X are not high compared with similar industries, but can be improved further more with mitigation measures such as installing a solar panel, and constructed wetland.

A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location

This work focuses on a hybrid preference-based electric vehicle charging station location problem, which considers multiple optimization preferences of distribution network operators, charge station owners, and electric vehicle users. The problem is formulated by an uncertain mixed-integer programming model. Due to the multi-fold uncertainty of the charging process, the uncertain model parameters are expressed as Type-2 fuzzy variables (T2-FVs). The critical value-based type reduction method is adopted to handle the high computational complexity. The proposed uncertain model is converted to its equivalent deterministic chance-constrained programming model. The deterministic counterpart is solved by General Algebraic Modeling System (GAMS). At last, numerical simulations are performed to demonstrate the proposed location strategy as well as some sensitivity analyses. The results indicate that for any given parameters, the equivalent deterministic model follows the general form of mixed-integer programming one that can be easily solved by GAMS. The proposed methodology can effectively handle the multi-fold uncertainty of the charging process. Compared with crisp models, the proposed location strategy can provide more robust location decisions for electric vehicle charging stations (EVCSs). In addition, we also found that different interest groups have conflicting preferences for the locations of EVCSs, so considering multiple hybrid optimization preferences is necessary.