Integration Of Vehicles Research Articles

Visualization and Visual Analytics in Autonomous Driving.

Autonomous driving is no longer a topic of science fiction. Advancements of autonomous driving technologies are now reliable. Effectively harnessing the information is essential for enhancing the safety, reliability, and efficiency of autonomous vehicles. In this article, we explore the pivotal role of visualization and visual analytics (VA) techniques used in autonomous driving. By employing sophisticated data visualization methods, VA, researchers, and practitioners transform intricate datasets into intuitive visual representations, providing valuable insights for decision-making processes. This article delves into various visualization approaches, including spatial-temporal mapping, interactive dashboards, and machine learning-driven analytics, tailored specifically for autonomous driving scenarios. Furthermore, it investigates the integration of real-time sensor data, sensor coordination with VA, and machine learning algorithms to create comprehensive visualizations. This research advocates for the pivotal role of visualization and VA in shaping the future of autonomous driving systems, fostering innovation, and ensuring the safe integration of self-driving vehicles.

Overview of Opportunities and Challenges to Vehicle-to-Grid Integration and Bahrain Perspective

Transportation sector is among the largest consumers of fossil fuels, which also makes it a significant contributor to the greenhouse gas emissions. A great deal of effort has been made to reduce this reliance of transportation on fossil fuel for its operation. These efforts have resulted in innovations in vehicle technology which include vehicles operating on electric and hybrid energy. The advent of these technologies has showed the way towards new possibilities to further increase the efficiency of energy systems and their utilization. One such system utilizes the integration of electric vehicles with the national grid system, it is referred to as vehicle-to-grid (V2G) technology. This technology provides a cost-effective and efficient solution to address the problems associated with the high demand and variable supply of renewable energy systems. This study provides a brief overview of the research conducted in this area, consequently identifying the opportunities and challenges related to this technology. Research shows that V2G integration has environmental benefits in the form of reducing CO2 emissions and reducing reliance on traditional energy sources. In spite of this, the economic feasibility of this technology requires efforts for standardization, infrastructure expansion and smart charging strategies. This paper is expected to provide future direction of work for all stakeholders, including academia, industry, and governmental bodies. There are collaborative efforts required to standardize the vehicle and grid technology and infrastructure expansion, while the research needs to focus on optimization strategies. In the end, discussion on the implementation of this technology in Bahrain is also presented. In this context, it was found that the major challenges are due to the current reliance on fossil fuel and the trend of increase in car ownership which must be addressed before taking steps towards V2G systems.

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

Integration of Electric Vehicles and Renewable Energy in Indonesia’s Electrical Grid

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia’s grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia.

A Review on Highway Network System - Samruddhi Expressway

Highway networks serve as critical arteries of transportation infrastructure, facilitating the movement of goods and people across vast geographical regions. As traffic volumes continue to increase and technological advancements reshape transportation systems, there is a pressing need to optimize the efficiency, safety, and sustainability of highway networks. This abstract presents a comprehensive system approach aimed at addressing these challenges. The proposed highway network system integrates advanced technologies such as intelligent transportation systems (ITS), real-time traffic monitoring, data analytics, and machine learning algorithms to optimize traffic flow, minimize congestion, and enhance safety. By leveraging data from various sources including sensors, cameras, and connected vehicles, the system enables proactive traffic management and congestion prediction, allowing authorities to implement timely interventions and mitigate potential disruptions. Furthermore, the integration of artificial intelligence and machine learning algorithms enables predictive maintenance of infrastructure, identifying potential issues before they escalate into critical failures. This proactive approach not only reduces maintenance costs but also enhances the reliability and lifespan of highway assets. Moreover, the system prioritizes safety through the implementation of automated collision avoidance systems, adaptive speed control, and real-time incident management. By leveraging vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication technologies, the system enables vehicles to exchange critical information, such as hazardous road conditions or impending collisions, thereby reducing the risk of accidents and improving overall safety. Additionally, the system incorporates sustainability principles by optimizing traffic flow patterns to minimize fuel consumption and emissions, promoting eco-friendly transportation practices, and facilitating the integration of electric and autonomous vehicles into the existing infrastructure.

Resolving Complexities in the Integration of Autonomous Vehicle

Smart vehicles become increasingly prevalent, there is a growing need for sophisticated monitoring systems that go beyond basic parameters. Current Smart EV monitoring systems often lack features that address critical aspects such as engine temperature, drunk and drive prevention, and immediate accident response. There is a pressing need for an all-encompassing monitoring system that enhances both vehicle performance and user safety. This project introduces an advanced Smart E-Vehicle Monitoring System designed to offer a comprehensive suite of features, including engine temperature monitoring, drunk and drive testing, accident detection, and remote control via mobile devices. The system not only ensures optimal vehicle performance but also prioritizes user safety through proactive anomaly detection. Keywords: Smart Vehicles, Electric Vehicles, monitoring systems, performance, Engine temperature, mobile devices.

Algorithms in the Margins: Organized Community Resistance to Port Automation in the Los Angeles Harbor Area

Public deliberations on artificial intelligence and machine learning (AI/ML) provoke strong interest in automation, or the perceived displacement of human labor due to general technological advances. Social science scholarship on well-compensated technology workers and a growing global underclass suggests automation remains largely a myth, with emerging technologies generating new forms of computational and emotional work. But little research has examined automation from the vantage point of community members at perceived greatest risk of displacement, such as those sustained by generations of logistics labor and physically located in racialized, underserved areas. In the United States and around the globe, logistics work is directly targeted by “automation engineers” to reduce labor costs on behalf of industry interests, posing a severe threat to port workers and their respective communities. This article examines organized community resistance to automation within the Los Angeles Harbor Area, drawing on four public hearings following the Port of LA’s planned integration of autonomous vehicles for the movement of cargo (i.e., automated straddle carriers). We find community mobilizations widely sought to enlarge public consideration of AI/ML to encompass matters of societal well-being and the collective good. These include shifting from the legality of AI integration to underscore public morality in light of anticipated harm, in addition to emphasizing the invisible social arrangements surrounding the Port and nearby community life. Our research reveals how lay people may reassert their own importance when confronted by technoscientific injustice, such as by reframing the future of work toward an insistence on “the future of our communities.”

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

Distributed Localization for UAV–UGV Cooperative Systems Using Information Consensus Filter

In the evolving landscape of autonomous systems, the integration of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has emerged as a promising solution for improving the localization accuracy and operational efficiency for diverse applications. This study introduces an Information Consensus Filter (ICF)-based decentralized control system for UAVs, incorporating the Control Barrier Function–Control Lyapunov Function (CBF–CLF) strategy aimed at enhancing operational safety and efficiency. At the core of our approach lies an ICF-based decentralized control algorithm that allows UAVs to autonomously adjust their flight controls in real time based on inter-UAV communication. This facilitates cohesive movement operation, significantly improving the system resilience and adaptability. Meanwhile, the UAV is equipped with a visual recognition system designed for tracking and locating the UGV. According to the experiments proposed in the paper, the precision of this visual recognition system correlates significantly with the operational distance. The proposed CBF–CLF strategy dynamically adjusts the control inputs to maintain safe distances between the UAV and UGV, thereby enhancing the accuracy of the visual system. The effectiveness and robustness of the proposed system are demonstrated through extensive simulations and experiments, highlighting its potential for widespread application in UAV operational domains.

Smart transportation systems using learning method for urban mobility and management in modern cities

The pressing demand for an escalation in renewable energy production arises from the surge in power consumption, escalating environmental concerns, and the exhaustion of fossil fuels. Microgrids, featuring dispersed power sources, exhibit superior resilience, efficiency, and stability in contrast to conventional power grids. Noteworthy investments in eco-friendly initiatives have been made by various major corporations, particularly in institutional structures. The current research advocates for the integration of photovoltaics, energy storage, and electric vehicles (EVs) as pivotal elements in a smart grid. This study introduces an optimal energy management system (EMS) designed to efficiently allocate power from existing sources. To address the challenges within an uncertain and reliable environment, a stochastic framework leveraging the whale optimization algorithm and unscented transform has been formulated. All simulations have been executed using the digital twin of the smart grid components. Results suggest that the proposed EMS ensures uninterrupted power supply, contributing to a substantial 45% reduction in energy costs. Furthermore, the potential of EVs as storage devices is explored. When employed as energy sources, EVs led to a noteworthy 45.58% reduction in power prices, while their use as loads still yielded a significant 19.33% decrease. The study also delves into the analysis of outage effects on the continuous power supply cost.

Evaluating EV User Behavior on Aggregator Smart Charging with ESS and Real-Time Pricing-Based Demand Response

With rising global electricity consumption, governments prioritize energy efficiency and the integration of electric vehicles (EVs) into energy markets. This study evaluates EV aggregator strategies using a smart charging method that modulates charging power rates based on user preferences. Simulations in Quito's distribution system assess various actions' impacts on aggregator costs and technical conditions. The study focuses on demand response (DR) strategies, particularly for residential areas, exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options. It introduces a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies, incorporating bi-directional EV and energy storage system (ESS) use. A novel mixed-integer linear programming (MILP) model for home energy management (HEM) integrates distributed renewable energy, V2H/V2G capabilities, and two-way ESS energy trading and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

Optimal sizing of multi-energy microgrid with electric vehicle integration: Considering carbon emission and resilience load

To address the intermittency of renewable energy sources, global warming, and increasing load demands, this paper proposes the optimal sizing of a multi-energy microgrid (MEMG) consisting of electrical, thermal, cooling, and hydrogen networks. The system integrates multi-energy storage and EVs along with the resilience load to facilitate a robust operation scheme. The paper introduces an improved resilience backup mechanism for EVs using hybrid storage. Then, random samples for stochastic parameters are generated with Monte Carlo Simulations (MCS). To this end, a mixed-integer linear programming-based model is developed to minimize cost, emissions, and load shed. Then, a CPLEX solver is applied to solve it efficiently. The optimal MEMG with the hydrogen network reduces cost by 4% and emissions by 40%. The case study validates that hybrid storage (BES-HST-TST) can effectively reduce the electricity grid purchase to zero, making MEMG self-sufficient while yielding the least annual system cost (ASC) of 3855562$ and decreasing emissions to 8385 kg, resulting in economic savings, environmental sustainability, and increased utilization of renewables. Notably, V2G can save 0.7546% of MEMG extra cost incurred due to EVs integration and significantly reduces CO2 emissions by 4%. A novel finding is that the proposed hybrid storage backup mechanism can effectively minimize the extra cost of keeping the resilience load by retaining 31.46% of backup in hydrogen form. It mitigates risk associated with power outages while achieving the least ASC of 3858933$ and CO2 emissions of 8043 kg. These results can help realize a green, cost-effective, and efficient energy system.

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study’s findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%).

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

The automotive industry has witnessed a significant increase in cyber threats as vehicles become more connected and reliant on software-driven systems. To safeguard against these threats, effective cybersecurity measures must be implemented. This paper explores the use of Support Vector Machine (SVM) algorithms as a means of bolstering automotive cybersecurity attack prevention. SVM algorithms have demonstrated remarkable capabilities in various domains due to their ability to handle complex and high-dimensional data. By leveraging SVM algorithms, this research aims to enhance the detection and prevention of cybersecurity attacks targeting automotive systems.
 The proposed approach involves training SVM models using labeled datasets that include both normal and anomalous driving scenarios. By analyzing various features and patterns extracted from the data, the SVM models can learn to differentiate between normal and malicious behaviour. These models can then be used in real-time to identify and mitigate potential cyber threats.
 The results of this study is compared with other machine learning algorithm like Logistic Regression, Naive Bayes, KNN, Decision Tree, highlight the effectiveness of SVM algorithms in detecting and preventing automotive cybersecurity attacks. The models exhibit high accuracy rates in distinguishing between normal and anomalous behavior, providing a robust defense mechanism against potential threats.
 In conclusion, this research emphasizes the significance of leveraging SVM learning algorithms for automotive cybersecurity attack prevention. By harnessing the power of machine learning, automotive systems can be fortified against cyber threats, ensuring the safety and integrity of vehicles and their passengers.

Arrive and retreat: London’s ultra-prime apartment blocks as dark urban renaissance

The expansion of an archipelago of residential blocks across London’s West End has created an urban renaissance of sorts, but it is a rebirth that has targeted the expanded ranks of the global super-rich, yielding a visibly luxurious, often vacant residential remaking, rather than the aspirations for a diverse, vibrant and open urbanism envisaged some years ago for the city. New luxury apartment blocks offer high levels of privacy, security and the seamless integration of bodies and vehicles into underground or enclosed entry points. These ‘ultraland’ blocks are less places of conviviality and encounter, offering instead a kind of dark space urbanism that facilitates a partial social engagement with local environs by barely present owners. Combining an analysis of the planning applications for these developments with extensive street observation in London’s West End we discuss the social and spatial integration of these developments. We argue that a key effect is one of social ‘reduction’—the sense of null spaces that absorb bodies, furnishings and cars in ways that are the antithesis of earlier ambitions for socially vital and democratic urban spaces. We discuss the significance of these changes for contemporary life in the city more broadly.

Digital Twin and Cyber-Physical System Integration in Commercial Vehicles: Latest Concepts, Challenges and Opportunities

Digital Twin and Cyber-Physical System Integration in Commercial Vehicles: Latest Concepts, Challenges and Opportunities

Unified Framework for the Analysis of the Effect of Control Strategies on On-Load Tap-Changer’s Automatic Voltage Controller

With the advent of new loads and generation on the low voltage grid, voltage fluctuation has increased, especially in active distribution grids with a high penetration of distributed resources and a large deployment of electric vehicles. The coordination of different technologies has emerged as the best way for voltage regulation, among others, smart inverters, open soft points or transformers with on-load regulation capability. This paper proposes a novel way to model the control strategies for the automatic voltage controller of On-Load Tap-Changer transformers. The purpose is to standardize and simplify the way these strategies are represented, in order to facilitate (i) their selection by Distribution System Operators, (ii) their future integration with other systems, and (iii) to increase the ability to anticipate On-Load Tap-Changer behavior. The proposal has been validated using real data, obtaining an accuracy of 99.15% in the tap changer positions. A unified framework is also introduced, which allows the proposed functional representation to be combined with the On-Load Tap-Changer controller behavior estimation. A experimental validation has been carried out where more than 150 000 strategies have been simulated, finally determining the one that best fits the objectives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Historically, on-load tap-changing transformers have been used in high-voltage substations. However, with the integration of electric vehicles and the penetration of distributed energy resources, the need to implement this type of solution in low-voltage substations has grown. However, the characteristics and requirements are not the same, for example, given their nature, low voltage grids are more unbalanced and are often regulated to ensure the quality of supply. Therefore, inheriting the control strategies of traditional on-load tap changers may pose a serious risk. This paper proposes a novel unified framework that facilitates the modeling and simulation of almost any control strategy for distribution transformers with on-load tap changers. This will allow the choice of control parameters that minimize voltage deviation from the voltage setpoint and maximize device lifetime. Our proposal can be considered as a solution to the uncertainty of which control parameters to use. It can be performed before commissioning, based on historical data, or a posteriori, based on the collected data.

Blockchain technology in the renewable energy sector: A co-word analysis of academic discourse

The transformative potential of blockchain technology in the renewable energy sector is increasingly gaining recognition for its capacity to enhance energy efficiency, enable decentralized trading, and ensure transaction transparency. However, despite its growing importance, there exists a significant knowledge gap in the holistic understanding of its integration and impact within this sector. Addressing this gap, the current study employs a pioneering approach, marking it as the first comprehensive bibliometric analysis in this field. We have systematically examined 390 journal articles from the Web of Science database, covering the period from 2017 through the end of February 2024, to map the current landscape and thematic trajectories of blockchain technology in renewable energy. The findings highlight several critical thematic areas, including blockchain's integration with smart grids, its role in electric vehicle integration, and its application in sustainable urban energy systems. These themes not only illustrate the diverse applications of blockchain but also its substantial potential to revolutionize energy systems. This study not only fills a crucial gap in existing literature but also sets a precedent for future interdisciplinary research in this domain, bridging theoretical insights with practical applications to fully harness the potential of blockchain in the renewable energy sector.

OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

Optimizing 2D path planning for unmanned aerial vehicle inspection of electric transmission lines

The inspection of electric power transmission lines using unmanned aerial vehicles represents an innovative maintenance approach that several countries, including Morocco, are considering adopting. The primary aim is to enhance existing procedures, capitalize on their advantages, and address challenges associated with traditional methods. Given the precision, efficiency, and safety required for this application to ensure reliable results, we propose a functional architecture for unmanned aerial vehicle-based electric power transmission line inspection. This architecture encompasses key phases such as data collection, planning, and inspection, while identifying electromagnetic interferences and minimum approach distances as major constraints. To streamline mission planning, we introduce a trajectory optimization algorithm integrated into a mission planning assistance application for transmission line inspection. This algorithm presents a novel approach by hybridizing three algorithms: genetic algorithm, ant colony optimization and particle swarm optimization. Consequently, our work represents a significant contribution to the integration of unmanned aerial vehicles in electric power transmission lines inspection operations for electrical energy transport, facilitating potential advancements and discoveries in the field.