Integration Of Vehicles Research Articles

Brain-inspired Collaborative Automatic Generation Control with Large-scale Electric Vehicles Integration

Brain-inspired Collaborative Automatic Generation Control with Large-scale Electric Vehicles Integration

DSM Solutions for Distribution Networks with Electric Vehicle Integration

DSM Solutions for Distribution Networks with Electric Vehicle Integration

Analysis of electric vehicle integration in the Indian grid system

Analysis of electric vehicle integration in the Indian grid system

Stress tolerance of lightweight glass-free PV modules for vehicle integration

Electric vehicles (EVs) currently dominate the sales in the automotive market. A big leap in this market can be made by developing a photovoltaic product that can be integrated to an EV, as it can boost the driving range of the EV while reducing the charging frequency. Such vehicle-integrated photovoltaic (VIPV) products are already successfully demonstrated, but they are usually made with glass as a front sheet – making them bulky and limiting their use to the car roofs due to safety reasons. The contemporary focus of the research in the field of VIPV is on developing a product that is lightweight (LW) and easily integrable into the complex shapes of an EV. Therefore, in this work, we present our initial findings on a novel architecture for LW VIPV modules employing polycarbonate (PC) as a front sheet. The mechanical behaviour of the LW module under bending is successfully simulated using finite elements (FE) modelling to predict the fracture of the solar cells, which can then be used as a predictive tool to check the maximal load on the PV body of an EV before cracking the c-Si solar cells. We demonstrate that a change in the temperature of the PC-based LW modules can modify the interspacing between the cells and thus create stress on the connectors. The dog-bone connectors are found to allow almost unconstrained movement of the cells in the module when subjected to variation of temperature. The cell movements may result in mechanical fatigue of the interconnection, which can ultimately result in disconnection of the cells. Initial performance of the dog-bone connectors is investigated by applying mechanical fatigue experiments, which demonstrate that the special geometry of the dog-bone connector could endure a greater number of thermal cycles than a simple prismatic shape would.

Інтеграція БпЛА зі нейронною мережею у завданнях із виявлення наземних мін та мінних полів

One of the newest stages in the improvement of unmanned aerial vehicles (UAVs) is the integration of such systems with the neural networks, which, in turn, is not a novelty, but provides such systems with a further level of practical application. Having conducted a meta-analysis of the results of previous studies and available information on this topic, it was found that in the modern period, in addition to successful practical implementations of the integration of artificial intelligence with UAVs, there is already a certain classification of such processes according to the principles of optimal improvement of UAV capabilities and by areas of society. In addition to the publicly available and well-known information about the successful use of drones in the military and logistics sectors of human activity, UAVs successfully perform tasks in such sectors as agriculture, engineering, search, etc. The main purpose of the article is to analyze, review, study and systematize existing information on the positive effectiveness and feasibility of using the principles, approaches and integration of unmanned aerial vehicles with machine learning technologies to improve the efficiency of solving the problems of locating and detecting landmines and minefields, which is a major humanitarian problem for civil society located in the territory where military conflicts are currently taking place or in the territories where military clashes or conflicts have occurred in the past. In this article, a small study was conducted to develop a prototype neural network that can be further integrated with UAVs for landmine and minefield detection tasks. The described neural network was trained on an open dataset, trained using the algorithms chosen in the study, and has a fairly good final result in terms of detection accuracy, which is 1.5% higher than the accuracy of publicly available neural networks in a review of similar developments or studies.

Shifting Perceptions and Emotional Responses to Autonomous Vehicles Using Simulated Experiences.

The societal integration of autonomous vehicles (AVs) relies on public acceptance, closely related to individual emotions and perceptions. This study explores the emotional factors affecting AV acceptance in Spain through lexical tasks, virtual AV simulations, and questionnaires, surpassing traditional attitude surveys by examining subtle emotional and lexical reactions to AVs. Acceptance was measured in terms of AV knowledge, perception of autonomous driving, and safety, with emphasis on lexical-emotional analysis after simulation. Findings indicate gender differences in AV acceptance, with women showing less knowledge and comfort with AV technology. Simulation improved understanding and generated more positive responses. This study shows how lexical tasks can reveal emotional influences on AV perception and suggests a wider approach to assess technology acceptance. These findings aid in creating campaigns and experiences to enhance public AV acceptance, mindful of demographic differences. Future studies should extend this framework to various populations to investigate the emotional lexicon's role in AV acceptance.

Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles

As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human drivers. The experiments involved driving at multiple constant speeds to evaluate the efficacy of ACC in maintaining safe following distances. Our findings indicate that ACC systems consistently converge on optimal following distances, demonstrating their ability to regulate spacing between vehicles effectively. However, a notable downside emerged in terms of their adverse impact on road capacities, where the results indicate a mitigation in capacity percentages of 7.6%, 9.3%, and 15.6% for the three types of ACC-equipped vehicles compared to human drivers. This study sheds light on the intricate interplay between ACC systems and human driving behaviors, emphasizing the need to consider both factors when envisioning the future of autonomous vehicles. While ACC systems provide a standardized and reliable approach to following distances, the shorter distances observed in human-driven scenarios suggest a potential trade-off between safety and traffic capacity. These insights contribute to a comprehensive understanding of the dynamics involved in autonomous driving, facilitating informed decision making for the integration of autonomous vehicles into future transportation systems.

Optimizing Nonlinear Control Strategies for Autonomous Vehicles

Maximizing the effectiveness of nonlinear control techniques (NCT) is crucial for the broad acceptance and smooth integration of autonomous vehicles. Navigating the intricacies of real-world settings, boosting vehicle stability, and enhancing overall performance are all greatly impacted by these tactics. For manufacturers to successfully deploy autonomous cars in environments that are both dynamic and unpredictable, it is crucial to strike a balance between rigid control and flexibility. Autonomous vehicles confront a number of obstacles when trying to optimize nonlinear control systems, including changing road conditions, unpredictable traffic, and the requirement to make decisions in real-time. Because of the complexity of these problems, traditional control methods are frequently not up to the task, thus one need to find new ways to make control strategies more resilient and flexible. A Nonlinear Control Optimization Framework (NCOF) is suggested in this research; it makes use of adaptive control mechanisms, high-level optimization methods, and machine learning approaches. NCOF is engineered to enhance the vehicle's performance in a variety of situations by continuously adapting and optimizing control parameters using environmental data and real-time feedback. Beyond conventional navigation, the NCOF finds use in situations including emergency response, complicated urban surroundings, and bad weather. Improved decision-making, navigating, and passenger safety in a variety of situations are all outcomes of NCOF's optimization of nonlinear control techniques for autonomous cars. Thorough simulation assessments are carried out across several virtual scenarios to verify the efficacy of the suggested NCOF. Displaying the flexibility, stability, and efficacy of NCOF in various contexts, the simulations evaluate the optimal nonlinear control schemes' effectiveness in contrast to conventional methods.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.

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Experimental Analysis of the Current Distribution between Graphite and SiOx in Blend Electrodes for Lithium-Ion Cells

The demand for battery cells with increased energy density without sacrificing performance is driving research into investigating new active electrode materials. The addition of silicon oxide as a second active anode material alongside graphite, referred to as graphite/SiOx blend electrodes, offers a great opportunity to increase the specific capacity of lithium-ion cells. The stable structure of graphite in combination with the high theoretical capacity of silicon represents a promising improvement, for example to increase the driving range of electric vehicles.In this work, the electrochemical behavior during the lithiation and delithiation process of blend electrodes is analyzed. Two custom-made cells are connected in parallel, with each of the cells containing either pure graphite or pure SiOx as the working electrode. Both cells use similar counter electrodes. The experimental setup enables the measurement of the current distribution during charging or discharging of the parallel connection. The observed individual current flow to each cell represents the current flow to each active material. This allows conclusions to be drawn about of the electrochemical processes in a graphite/SiOx blend electrode [1].This work focuses on two effects of blend electrodes: First, the lithiation differences between the active materials with increasing charge rates. Changes in the lithiation distribution between the active materials are observed at higher currents. Second, the effect of hysteresis on the lithiation of blend electrodes. When stating the lithiation process with a fully or partially delithiated blend electrode, the increased delithiation potential of SiOx [2] causes differences in the lithiation distribution between the materials. The knowledge gained on graphite/SiOx blend electrodes contributes to a better understanding of lithium-ion cells and to the optimization of their integration in electric vehicles.

(Invited) Physical Analysis and Modeling of Wafer-to-Wafer Copper Hybrid Bonding at Sub-Micron Pitches

The bonding process has four sequential steps: plasma surface treatment, rinsing, bonding, and annealing. It is important to understand the details of all these steps to enable high performance bonding at sub-micron pitches. A multi-level back-end-of-line test vehicles at 1um and 0.5um pitches have been produced to evaluate the execution of the Tokyo Electron wafer-to-wafer (W2W) bonding tool platform. Subsequently several simulation and modeling approaches have been developed to better comprehend the impacts of different process conditions on the physical hybrid bonding results.Figure 1 shows the scanning electron microscope (SEM) image of the 1um bonding pad pitch Kelvin-via test structure that was successfully bonded and electrically tested. A median resistance of 0.44Ω has been measured for well-aligned 1um pitch kelvin-via measurements. Multiple variations of the test structures purposefully shift the top and bottom wafer patterning up to 100% mis-aligned to study the impact on electrical results from the bonding alignment. The corresponding electrical resistance, leakage and yield results provide statistically relevant feedback to improve the design and process integration of the test vehicle and W2W bonding platform.Maintaining a proper composition of the copper bond pads during plasma activation is necessary, especially if a lower anneal temperature post bond is desired. At low thermal budget bonding the oxides are not able to diffuse through any thick layer of modified copper formed between the pads during bonding which can significantly impede the electrical properties of the contact. The impact of plasma gas composition on the formation of surface copper species was determined using X-Ray Photoelectron Spectroscopy. For low temperature bonding, a simultaneous O2/H2 plasma treatment produces the thinnest copper oxide with the lowest theoretical resistivity. The activation gas chosen is based on the dielectric being used, but it has a significant impact on copper oxidation.If the bonding dielectric material is SiCN, then an O2 or O2/H2 mixed gas is best used for the activation process. The Cu2O content is less than the diffusion length for low temperature bonding for both gases, but the concentration of both CuO and Cu(OH)2 is high for the O2 treatment. At these concentrations, a low temperature O2 activation would create a copper contact with significant resistance. These results will help develop a plasma treatment to achieve good electrical contacts between hybrid bonded wafers with low thermal budget.A quantum chemistry simulation was developed to study how O/N-radicals modify the surface of SiO2 to increase the bond density. O atoms binding with H, N and C in the SiO2 films to create OH, CH2O, CO, HNO and nitic oxides (NO) groups. NO-groups are hydrolyzed to form additional surface OH during the rinsing step, increasing the density of Si-O-Si formation and total bonding energy. Atomistic molecular dynamic simulation also confirmed how nitrogen atoms introduce silanol and siloxane group to the surface during the surface activation treatment and the hydration reaction. The population effects of N atoms on bond formation were also studied, which indicated when more N atoms exist it will lead to improved bonding.Several plasma conditions were tested for the surface activation process to bond thermal silicon dioxide (TOX) to each other, and the bonding strength was calculated by the Maszara-Method after the annealing process. Comparing the bonding strength between TOX/TOX bond with N2 and O2 plasma under the same plasma conditions, the bonding strength using N2 plasma treatment is 1.7 times stronger than the bonding strength using O2 plasma treatment, indicating that the N2 plasma treatment is superior to the O2 plasma treatment.Finally, a multi-physics finite element model to simulate the post-bond distortion resulting from the wafer-to-wafer bonding process in room conditions was evaluated. The axisymmetric simulation model includes both the solid mechanics of the silicon wafers and the fluid mechanics of the air gap to accurately simulate the bond propagation dynamics. The two wafers, which may be warped by stressed films in preceding process steps, are initially separated at a small, fixed gap. As the center of upper wafer is pushed down by a striker, an adhesion model like Lennard-Jones potential simulates the contact propagation behavior between the two wafers. The simulation also includes other bonder process parameters and hardware designs to provide accurate distortion results. The outputs of the simulation are validated against a numerical model. The simulation model is also validated against direct wafer bonding experiment results. Figure 1

Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price

Accurate load control is the basis of safe operation and optimal dispatching in active distribution network, and the demand response system of flexible electricity price mechanism and incentive mechanism is the premise of load control. Aiming at the complexity increase of load control caused by the integration of conventional loads, electric vehicles, and micro-grid multi-type user loads in the existing active distribution network, this study comprehensively takes the power consumption willingness of multiple typesof users in active distribution network into account for the first time. This study dominated by the user willingness selects a typical active distribution network load-related data onto a certain area to conduct a calculation example analysis. Firstly, a multi-type user load demand response model of active distribution network is constructed. Then, in order to achieve flexible control of active distribution network loads, a dynamic time-of-use electricity price game model considering the target function and the willingness of multiple usersis proposed, in order to achieve the maximum of user satisfaction and interests of power supply enterprises. Furthermore, the example results are compared with the intrinsic electricity price.Finally, the simulation results show that the time-of-use price established by the multi-type user load game is effective inguiding users to adjust their power consumption habits, making the load curve shift, controlling the electricity load within the maximum power supply limit, and thus alleviating the pressure of power grid expansion.

A novel spatial electric load forecasting method based on LDTW and GCN

AbstractSpatial power load forecasting is crucial for power grid planning, generation planning, dispatching, efficient power utilization, and sustainable development. The integration of new energy sources and electric vehicles has significantly altered grid loads, increasing the complexity of spatial load forecasting. However, existing techniques fail to fully consider the temporal and spatial correlation characteristics of data, leading to challenges in data identification and summarization. This reduces load forecasting accuracy and prolongs prediction time. To address these issues, a spatial electric load forecasting method based on improved scale limited dynamic time warping (LDTW) and graph convolutional network (GCN) are proposed. Firstly, the improved scale LDTW is used to improve the clustering effect of K‐Mediods++, refine the type of load data, and make the subsequent model training more targeted. Secondly, the interconnections and distances of substations in a real network structure is used to build a graph model to capture the power load distribution. Finally, based on the clustering results and the graph model, GCN‐LSTM is used to construct the spatio‐temporal forecasting algorithm. The proposed algorithm is tested using load data from a region in Shanghai and compared with other advanced algorithms. Results show that the algorithm achieves higher prediction accuracy and efficiency.

Local centric energy estimate model for clean and affordable power generation in a solar integrated passenger vehicle

Clean energy generation from Vehicle Integrated Photovoltaic (VIPV) technology is prudent to foster future smart transportation in passenger cars. A VIPV vehicle remits a reliable energy generation scenario in static and dynamic modes. The estimation of irradiance hovering over a vehicle's surface exposure metrics is based on the diurnal sun-path at the local centric coordinate system (LCCS). By inferring the locations' analemma curve, the irradiance patterns can be derived. Literature reports that the irradiance patterns on a parked static vehicle (PSV) are unexplored till date, particularly under southern Indian conditions. In this study, we propose an LCCS for a static vehicle to understand the irradiance over the vehicle when parked in two different cardinal orientations at Vellore (12.9717° N, 79.1654° E), India. Further, experiments were conducted over a PSV facing due south of the location to validate the theoretical claims and factual irradiance patterns. The results reveal that theoretical observations and energy estimates were in proximity with experimental measurements by a deviation of <15 %. An estimated solar potential energy yield of up to 1.66 to 2.09 kWh per day can be attained from a PSV when integrated with thin-film flexible solar modules over the lateral surfaces.

Challenges of the Integration of Micromobility Vehicles into Modern Traffic and Transportation Systems

This paper presents the main challenges of integrating micromobility vehicles into modern traffic and transportation systems. Although micromobility seems to be an effective concept for the first and last mile, the reality points to the potential problems that the integration of micromobility vehicles can create and that must be resolved appropriately. Micromobility vehicles are characterised by extensive development, which is not accompanied by appropriate legal regulations. The street design has its spatial limitations and usually separates non-motorised and motorised users, which is a notion that could be disrupted by new micromobility options. When it comes to Serbia, the existing legislation does not recognise the majority of micromobility vehicles, which results in the lack of safety of these participants and their place in the street profile. The aim of this paper is to provide guidelines for improving the existing regulations and integrating these vehicles into the traffic system of Serbia, with special reference to general recommendations through which micromobility vehicles can be treated in other countries. The results of this paper can be useful to decision-makers but also to all other participants in the process of developing effective policies and strategies for the integration of micromobility vehicles into traffic and transportation systems.

Integration of Electric Vehicles (EVs) with Electrical Grid and Impact on Smart Charging

Integrating Electric Vehicles (EVs) with the electrical grid is a pivotal aspect of modern transportation systems. This integration poses multifaceted challenges and opportunities, influencing the grid's stability, energy management, and environmental sustainability. Smart charging solutions emerge as a crucial mechanism to address these challenges by optimizing charging patterns, balancing grid demand, and maximizing renewable energy utilization. This abstract delves into the impacts of EV-grid integration, exploring the interplay between smart charging solutions and grid dynamics while considering the implications for energy infrastructure, user behavior, and environmental impact. It also provides a concise overview of EV grid integration's key issues. It highlights the crucial role of smart charging technologies in addressing them and paving the way for a sustainable future of transportation and energy.

Review of Power Quality and Stability of EV Charging with Grid and PV Solar

As a sustainable mode of transportation, electric vehicles (EVs) are gaining popularity, and their integration with the power grid and photovoltaic (PV) solar systems is essential for their widespread adoption. Solar PV and EV systems have improved based on the generation and demands of the power grid. However, incorporating EV chargers with the grid and PV solar systems can cause power quality issues and stability problems that can impact the charger's performance and affect the charging procedure. EV technologies are divided into two major components: electric propulsion systems and EV charging systems. The electric propulsion system delivers the necessary energy for the EV motor in an EV system. In addition, with higher penetration, EVs and PVs are anticipated to emerge as new participants in the energy market operation and can play a crucial role in redefining the market. Many have integrated into the electricity network to reach renewable energy sources (RES), such as wind and solar PV. The study concludes that continued research and development are necessary to upgrade the power quality and solidness of EV charging frameworks, to ensure the seamless integration of PV solar systems and electric vehicles into the grid, and to promote sustainable transportation.

Integration of Unmanned Aerial Vehicle Imagery and Machine Learning Technology to Map the Distribution of Conifer and Broadleaf Canopy Cover in Uneven-Aged Mixed Forests

Uneven-aged mixed forests have been recognized as important contributors to biodiversity conservation, ecological stability, carbon sequestration, the provisioning of ecosystem services, and sustainable timber production. Recently, numerous studies have demonstrated the applicability of integrating remote sensing datasets with machine learning for forest management purposes, such as forest type classification and the identification of individual trees. However, studies focusing on the integration of unmanned aerial vehicle (UAV) datasets with machine learning for mapping of tree species groups in uneven-aged mixed forests remain limited. Thus, this study explored the feasibility of integrating UAV imagery with semantic segmentation-based machine learning classification algorithms to describe conifer and broadleaf species canopies in uneven-aged mixed forests. The study was conducted in two sub-compartments of the University of Tokyo Hokkaido Forest in northern Japan. We analyzed UAV images using the semantic-segmentation based U-Net and random forest (RF) classification models. The results indicate that the integration of UAV imagery with the U-Net model generated reliable conifer and broadleaf canopy cover classification maps in both sub-compartments, while the RF model often failed to distinguish conifer crowns. Moreover, our findings demonstrate the potential of this method to detect dominant tree species groups in uneven-aged mixed forests.

Enhancing load frequency control with plug-in electric vehicle integration in non-reheat thermal power systems

Enhancing load frequency control with plug-in electric vehicle integration in non-reheat thermal power systems