Integration Of Vehicles Research Articles

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

The rapid integration of renewable energy sources (RES) and the electrification of transportation have significantly transformed modern energy infrastructures, emphasizing the need for efficient and flexible energy management systems. This study presents an intelligent, variable-fed, Type-2 Fuzzy Logic Controller (IT2FLC) designed for optimal management of Hybrid Microgrid (HMG) energy systems, specifically considering different modes of Electric Vehicles (EVs) integration. The necessity of this study arises from the challenges posed by fluctuating renewable energy outputs and the uncoordinated charging practices of EVs, which can lead to grid instability and increased operational costs. The proposed IT2FLC is based on comprehensive mathematical modeling that captures complex interactions among HMG components, including Doubly Fed Induction Generator (DFIG) units, photovoltaic (PV) systems, utility AC power, and EV batteries. Utilizing a yearly dataset for simulation, this work examines the HMG’s flexibility and adaptability under dynamic conditions managed by the proposed intelligent controller. A Simulink-based model is built for this study to replicate the dynamical operation of the HMG and test the precise and real-time decision-making capability of the proposed IT2FLC. The results demonstrate the IT2FLC’s superior performance, achieving a substantial cost avoidance of nearly $3,750,000 and efficient energy balance, affirming its potential to sustain optimal energy utilization under stochastic conditions. Additionally, the results attest that the proposed IT2FLC significantly enhances the resilience and economic feasibility of hybrid microgrids, achieving a balanced energy exchange with the utility grid and efficient utilization of EV batteries, proving to be a superior solution for optimal operation of hybrid grids.

Hybridization effect on energy absorption capacity of composite crash boxes

AbstractCrash boxes are safety elements generally made of metallic materials and mounted on the chassis of vehicles to prevent possible injuries to driver and passengers by absorbing the energy generated during a collision and ensuring integrity of vehicle. In order to save fuel and reduce harmful gases released into the environment, the proportion of metallic materials used in vehicles is decreasing and being replaced by composite materials. While composite materials are as durable as metallic materials, they are preferred because they are lighter. In this study, circular composites consisting of glass (S1), aramid (S2), carbon (S3), and hybrid combinations of aramid‐carbon‐glass (S4), carbon‐glass‐aramid (S5), and glass‐aramid‐carbon (S6), all with epoxy resin matrices, were produced using the vacuum infusion method and investigated. Crush parameters, maximum peak loads and specific energy absorption of hybrid tubes with different fiber types and winding order were compared. S3 tube had the best energy absorption performance among all samples of 257.288 Joules, while the worst energy absorption performance was obtained for S2 tube with 30.944 Joules. Among the hybrid samples, the best result was determined as 166.50 Joule with hybrid S5. Although the energy absorption capacities of hybrid composite tubes were close to each other, they had lower results for energy absorption and specific energy absorption than S1 and S3. For specific energy absorption, as with energy absorption, the best result was obtained with S3, while the lowest result was obtained with S2.Highlights The aim of this study is to evaluate the performance of composite crash boxes. The effect of fiber type and hybridization was examined. Specific energy absorptions and maximum contact forces were compared. Cylindrical samples were produced by vacuum infusion. The variation of damage depending on fiber type was investigated.

Economic Energy Scheduling of electrical micro grid considering optimal participation of the electric vehicles

This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, taking these uncertainties into consideration to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimize cost of microgrid by 4.31%, 3.82% and 1.87% than genetic algorithm (GA), Particle swarm optimization (PSO) algorithm and Teaching learning-based optimization (TLBO) algorithm, respectively.

The influence of annealing temperature on the microstructure and mechanical properties of Fe-0.52 C-11Mn-5.14Al-1Cr lightweight steel

Steel used in the automotive industry must strike a delicate balance between strength and ductility to maintain the structural integrity of vehicles and achieve intricate designs. Traditional methods of reducing weight by using high-strength low-carbon steels have limitations. As a result, a new dual-phase lightweight steel with an austenite-ferrite structure and the composition Fe-0.52 C-11Mn-5.14Al-1Cr was developed. The relationship between the microstructure and mechanical properties of this lightweight steel, which underwent various annealing processes (700°C-1000°C) after cold rolling, was examined using specialized equipment like a universal testing machine, electron microscopy, and X-ray diffractometer. The findings reveal that the cold-rolled test steel (CR) exhibits high tensile strength (1500 MPa) but low elongation at break (3.9 %). The performance of the experimental steel post-annealing treatment has shown significant improvement, with the average grain size increasing with higher annealing temperatures. The γ phase generally shows an increasing trend, with finer grains post-annealing, and the distribution of the α phase displays a consistent pattern along γ boundaries. Additionally, a substantial number of annealing twins appear in the matrix. These twin boundaries absorb some dislocations, and slip leads to the movement of twin boundaries, effectively reducing stress concentration from deformation and enhancing the ability of twin boundaries to accommodate plastic strain, creating twin dislocation interaction. The interplay among dislocations, stacking faults, and twins regulates the balance between strength and plasticity. Following treatment at 850°C × 1 min + 500°C × 3 min, the steel displays excellent mechanical properties, including ultimate tensile strength (836 MPa), elongation (53 %), and strength-ductility product (43,723 MPa·%). This study identifies a specific heat treatment parameter that achieves a harmonious equilibrium between outstanding strength and plasticity (strength-ductility product) within the designed composition.

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

A Situated Inspection of Autonomous Vehicle Acceptance – A Population Study in Virtual Reality

The successful integration of Autonomous Vehicles (AVs) into daily life is influenced by various individual and contextual factors. This study examines key factors affecting acceptance and trust in AVs by assessing how interactive experiences influence user perceptions and adoption. Utilizing a Virtual Reality (VR) environment to simulate real-world driving conditions, the research investigates the impact of different levels of vehicle interaction—manual control, semi-autonomous, fully autonomous, and taxi-riding modes—on user and passenger trust, anxiety, perceived ease of use, and overall acceptance. A dual-method approach was employed, combining objective data collection through eye-tracking, which measures engagement and vigilance, with subjective user feedback gathered via the Autonomous Vehicle Acceptance Model (AVAM) questionnaire. The findings reveal a complex relationship between user control and trust in AV systems. While semi-autonomous conditions that incorporate user participation in decision-making enhance trust and intention to use, fully autonomous conditions influence trust but not intention to use significantly. However, semi- autonomous conditions also elicited higher anxiety levels compared to fully autonomous modes, suggesting a trade-off between interaction and comfort. Engagement levels, as indicated by visual metrics such as gaze patterns, were found to be critical for understanding user acceptance. These results highlight the importance of considering both subjective perceptions and objective behaviors in developing AV interfaces. The study contributes significant insights into the cognitive underpinnings of AV acceptance and underscores the need for strategies that both enhance user trust and minimize anxiety by finding an optimal balance between automation and user control. Further research into this balance is recommended to better accommodate the evolving nature of user trust and the cognitive complexities associated with semi-autonomous systems.

Power management system for a hybrid energy storage electric vehicle using fuzzy logic controller

This research work introduces a power management system for a hybrid energy storage system (PMHESS) configuration for urban electric vehicles utilizing a fuzzy logic controller (FLC). Consequently, the configuration includes two DC/DC interleaved converters that establish a connection between the battery and ultra-capacitors (UCs), thereby ensuring a substantial power capacity. The FLC is adaptable and sturdy, considering information from sources other than the vehicle, such as other vehicles or road infrastructure. The study explores incorporating road topography into the control structure to improve hybrid storage performance. Simulation results show that combining lithium batteries with UCs improves the energy source’s performance and reliability. The power management algorithm reduces sudden demands on the battery by considering the slope of the ground. The work proposes energy storage integration for electric vehicles, exploring its benefits through simulations. Overall, the proposed hybrid energy storage system (HESS) demonstrates high efficiency and power for urban electric vehicles. The results are validated using MATLAB/Simulink.

A Holistic Approach to Cybersecurity Integration in Autonomous Vehicles

The breakthrough of the self-driving car technology is a double-edged sword that gives us incredible opportunities and at the same time, it is the source of huge cybersecurity challenges. This paper champions the creation of a complete legal system and worldwide standards, like the ISO 26262, to regulate different parts of the autonomous vehicle (AV) operations, such as safety testing, liability, data privacy, and cybersecurity. By highlighting the significance of cybersecurity from the very start, the stakeholders and the automakers have to incorporate the practices like secure programming, routine updates, intrusion detection systems and encryption methods which will be the effective shield against the unauthorized access and data breaches. People should be informed and educated about the cybersecurity risks of AVs so that they can take the steps to protect themselves. Nevertheless, the integration of AVs with the Internet of Things (IoT) and other devices makes the cybersecurity landscape even more complicated, which means that there is a constant need for research and development to deal with problems like adversarial attacks. Presently, the deep learning-driven AV systems do not have the explainability which complicates the safety-critical applications, hence the need to increase the resilience training and deepen the deep learning models against attacks. The paper goes through all the literature on AV cybersecurity which stresses the need for functional safety standards and cybersecurity engineering guidelines. Collaboration between the governments, the industry, and the researchers is a must to set the legal foundations and the technical details for the AV cybersecurity across all levels of automation. Finally, solid cybersecurity is the key to the success of the autonomous vehicle revolution, hence the road to transformative safety, efficiency, and sustainability is paved. INDEX TERMS: Autonomous vehicles, Cybersecurity, ISO 26262, Encryption methods, ISO 21434, EU regulations, Security and Privacy.

Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations.

Design and Development of Quadcopter for Agro-Chemical Spray in Agricultural Field

The advent of Agricultural Drones promises to revolutionize modern farming practices by employing autonomous drones for crop surveillance and pesticide application. Outfitting quadcopters with spraying mechanisms presents numerous advantages over traditional methods. Firstly, it facilitates precise and targeted application of agro-chemicals, thereby minimizing wastage and environmental pollution. Furthermore, quadcopters exhibit the capability to effortlessly navigate challenging terrains, accessing areas previously unreachable by conventional spraying equipment and ensuring comprehensive crop coverage. Additionally, their autonomous operation or remote human supervision enhances operational efficiency, resulting in reduced labor costs. This initiative explores the integration of unmanned aerial vehicles (UAVs) into agricultural practices, specifically emphasizing their role in precisely spraying agro-chemicals. The project encompasses the entire development cycle, from design and development to the deployment of customized agricultural drones capable of autonomous flight and accurate pesticide distribution. The incorporation of quadcopters for precise agro-chemical spraying marks a significant advancement in agricultural technology, offering precision, efficiency, and sustainability. Ongoing research, development, and collaboration play a crucial role in realizing the transformative benefits of this innovative approach, ultimately enhancing agricultural productivity and environmental stewardship.

Multi-Regional Integrated Energy Economic Dispatch Considering Renewable Energy Uncertainty and Electric Vehicle Charging Demand Based on Dynamic Robust Optimization

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future.

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

Communications and Data Science for the Success of Vehicle-to-Grid Technologies: Current State and Future Trends

Vehicle-to-grid (V2G) technology has emerged as a promising solution for enhancing the integration of electric vehicles (EVs) into the electric grid, offering benefits, such as distributed energy resource (DER) integration, grid stability support, and peak demand management, among others, as well as environmental advantages. This study provides a comprehensive review of V2G systems, with a specific focus on the role of the communication, as they have been identified as key enablers, as well as the challenges that V2G must face. It begins by introducing the fundamentals of V2G systems, including their architecture, operation, and a description of the benefits for different sectors. It then delves into the communication technologies and protocols in V2G systems, highlighting the key requirements in achieving reliable and efficient communication between EVs and the different agents involved. A comprehensive review of communication standards is described, as well as the main communication technologies, which are evaluated in terms of their suitability for V2G applications. Furthermore, the study discusses the challenges and environmental implications of V2G technology, emphasizing the importance of addressing strong and reliable communications to maximize its potential benefits. Finally, future research directions and potential solutions for overcoming challenges in V2G systems are outlined, offering useful insights for researchers, policymakers, and administrations as well as related industry stakeholders.

A novel predictive optimal control strategy for renewable penetrated interconnected power system

AbstractThe stable and efficient regulation of voltage and frequency is a critical issue associated with the functioning of modern power system network having renewable integration. This manuscript focuses on the concurrent regulation of voltage and frequency in a power network comprising multiple generating sources. Herein, along with conventional thermal and diesel plants, renewable generations from different sources have been considered such as wind and solar photovoltaic (PV) resources. Further, in a fresh attempt, the authors have modeled wind and solar PV generation using two different stochastic modeling methods concerning combined voltage and frequency loops in the regulated power system model. Furthermore, the novel Leader Harris Hawks Optimized Model Predictive Controller (MPC‐LHHO) has been applied to concurrently minimize the voltage and frequency deviations in the given power network. To address the intermittent nature of load and renewable generations, various auxiliary frequency controllers, such as the unified power flow controller (UPFC) and electric vehicle (EV) integration with the grid, have been implemented. The robustness of the proposed control method has been successfully validated through diverse scenarios of random loadings.

Graph‐based solution for smart grid real‐time operation and control

Graph‐based solution for smart grid real‐time operation and control

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.

Analyzing EV User Behavior in Aggregator Smart Charging with ESS and Real-Time Pricing

Amidst increasing global electricity consumption, governments prioritize energy efficiency and electric vehicle (EV) integration into energy markets. This study examines EV aggregator strategies employing smart charging methods, adjusting charging rates based on user preferences. Through simulations in Quito's distribution system, it analyzes actions' effects on aggregator costs and technical conditions, with a focus on demand response (DR) strategies, particularly in residential areas. Exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options, the study introduces a collaborative evaluation of dynamic pricing and peak power limiting-based DR strategies, integrating bi-directional EV and energy storage system (ESS) use. It also proposes a novel mixed-integer linear programming (MILP) model for home energy management (HEM), incorporating distributed renewable energy, V2H/V2G capabilities, 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.

Integrating AI-Technology with Unmanned Aerial Vehicles for Traffic Congestion Recognition

This article explores the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technology to address traffic congestion. By leveraging UAVs equipped with route-planning capabilities, aerial images of traffic scenes are captured and analyzed using convolutional neural networks (CNNs). The processed data is then transmitted to a traffic management center, enabling timely recognition and response to congestion. This framework offers a practical approach to alleviate traffic delays, reduce fuel consumption, mitigate pollution, and enhance overall road safety

The Environmental Sustainability Potential of Autonomous Vehicles: An Overview

The optimization of transportation systems and the integration of autonomous vehicles (AV) are significantly transforming urban mobility and exerting outstanding effects from an environmental perspective. This article examines the possibilities of autonomous vehicles in reducing traffic congestion, emissions, and energy consumption. The optimized driving style of AVs, dynamic route planning, and enhanced intersection systems have a profound impact on emission reduction. The article also delves into current development trends and challenges, encompassing advancements in AV sensing technologies, traffic safety, and cybersecurity. These findings collectively suggest that the deployment of autonomous vehicles brings substantial benefits to sustainable urban mobility; however, further development is necessary to support the widespread adoption of AVs and strengthen societal trust.