Solar Vehicle Research Articles

Nolinear Static Aeroelastic Analysis and Optimization for High-Altitude Solar-Powered UAV With Large Aspect Ratio

According to the static aeroelastic characteristics of Solar-Powered UAV (SP-UAV) with large aspect ratio, this paper develops a nolinear aeroelastic analysis method which takes into account both the calculation accuracy and efficiency. Firstly, we establish the finite element model of the geometric nonlinear structure, which is verified by the static test of the whole UAV to reduce the calculation error caused by the simplified method of structure model. Then, the aerodynamic model of SP-UAV is established by using Curved Vortex Lattice (CVL), and the structure-aerodynamic coupling interpolation is realized by the Thin Plate Spline-interpolation (TPS) method, so as to complete the full static aeroelastic analysis. The analysis indicate that the calculated results of the longitudinal trim are well match the first flight condition. In particular, considering the elevator efficiency loss occurred in longitudinal trim analysis of the UAV, we design an optimization scheme to improve the efficiency of the elevator by about 15%. The simulation results show that the loss of elevator efficiency of flexible solar UAV with high aspect ratio is non-negligible, with the maximum value higher than 40% under high dynamic pressure. In actual flight, the speed of UAV must be reduced as much as possible to minimize the loss of rudder efficiency, while the elastic control method should be considered in flight control.

Energy Management System for Hybrid Renewable Energy-Based Electric Vehicle Charging Station

This paper introduces an energy management algorithm for a hybrid solar and biogas-based electric vehicle charging station (EVCS) that considers techno-economic and environmental factors. The proposed algorithm is designed for a 20-kW EVCS and uses a fuzzy inference system in MATLAB SIMULINK to manage power generation, EV power demand, charging periods, and existing charging rates to optimize real-time charging costs and renewable energy utilization. The results show that the proposed algorithm reduces energy costs by 74.67% compared to existing flat rate tariffs and offers lower charging costs for weekdays and weekends. The integration of hybrid renewables also results in a significant reduction in greenhouse gas emissions, with payback periods for charging station owners being relatively short, making the project profitable.

Kinematic analysis and structural design of suspension wishbone for a solar electric vehicle

Kinematic analysis and structural design of suspension wishbone for a solar electric vehicle

Kinematic analysis and structural design of suspension wishbone for a solar electric vehicle

Kinematic analysis and structural design of suspension wishbone for a solar electric vehicle

Grid-Interfaced Solar PV Powered System for Electric Vehicle Battery Application

The main aim of the project is to develop a system to charge the electric vehicle battery continuously and to control the three phase grid system. The perturb and observe method is used to get the maximum power from the solar PV array. The electric vehicle battery is connected at bi directional converter at the DC link and the Dc link is also connected at to the voltage source converter, this electric vehicle battery gets charged at low load demand and discharged at high load demand. This converter usually maintains the maximum power at the DC link by this the electric vehicle battery can be charged by taking a low rated battery to store energy of extra power. An adaptive recursive digital filter is used in better way to the maximum results with grid-connected solar PV-Electric vehicle battery system. The system with recursive filter control is controllable when the solar insolation is changing and load demand change. VSC works efficiently without any disturbance under no solar power generation and transfers the reactive power to the grid. The project is done with the help of simulation in MATLAB and test results on a hardware prototype is done through both in steady state conditions and dynamic conditions.

Research on the Application of Vortex Lattice Method and Doublet Lattice Method in Aerodynamic Analysis of the Wing of Solar-Powered Unmanned Aerial Vehicle

In this paper the application of doublet lattice method and vortex lattice method is discussed. A wing of a solar-powered unmanned aerial vehicle (UAV) is chosen as the model. Both the methods are used to calculating the pressure distribution. The results show that the two methods can be used for unsteady aerodynamic analysis of such aircraft in time domain and frequency domain respectively.

Hybrid Power Systems in Multi-Rotor UAVs: A Scientific Research and Industrial Production Perspective

This study is conducted in the context of two significant challenges facing Unmanned Aerial Vehicle (UAV) technologies. These challenges are limited flight duration and payload, which have a direct impact on the performance and areas of application. The Hybrid Power System (HPS) has a prominent role in overcoming these challenges with a view to expanding the application fields of UAVs to include further areas such as air taxis and air ambulance services. This work first presents a systematic review of the scientific contributions in the past two decades with the aim of exploring the existing design approaches for HPSs in multi-rotor UAVs. To do this, a Systematic Literature Review (SLR) has been done. The selected articles have been reviewed, analyzed, evaluated, and then classified according to the scope of the study, research trends, design methodologies, main findings/shortcomings, and future work. Retrieved data shows that the Internal Combustion Engine (ICE) and the Proportional-Integral-Derivative (PID) controller were most commonly used. Fuel Cells (FCs) still need more research and experiments to overcome practical constraints, while solar-powered UAVs require more surface areas to absorb large amounts of solar radiation. To keep up-to-date with technological advances in this area, this research also surveys, analyzes, and discusses the available commercial models of HPS that can be used in UAVs along with some examples of hybrid-powered UAVs. The intended goal of this paper is to stimulate and help researchers and those interested in the challenges of electric UAVs and guide them to develop this field more efficiently from an industrial/scientific perspective.

Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method

Conventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO2 emissions. Research shows that by 2030, EVs can reduce CO2 emissions by 28%. However, two major obstacles affect the widespread adoption of electric vehicles: the high cost of EVs and the lack of charging stations. This paper presents a comprehensive data-driven approach based demand-side management for a solar-powered electric vehicle charging station connected to a microgrid. The proposed approach utilizes a solar-powered electric vehicle charging station to compensate for the energy required during peak demand, which reduces the utilization of conventional energy sources and shortens the problem of fewer EVCS in the current scenario. PV power stations, commercial loads, residential loads, and electric vehicle charging stations were simulated using the collected real-time data. Furthermore, a deep learning approach was developed to control the energy supply to the microgrid and to charge the electric vehicle from the grid during off-peak hours. Furthermore, two different machine learning approaches were compared to estimate the state of charge estimation of an energy storage system. Finally, the proposed framework of the demand management system was executed for a case study of 24 hours. The results reflect that peak demand has been compensated with the help of an electric vehicle charging station during peak hours.

Energy Autonomy Simulation Model of Solar Powered UAV

The energy autonomy of UAVs is an important direction in the field of aerospace. Long-endurance aerial vehicles allow for continuous flight; however, to meet the guidelines, the power supply system has to be able to harvest energy from outside. Solar cells allow the production of electricity during the day when the sun shines on their surface. Depending on the location, time, weather, and other external factors, the energy produced by PV panels will change. In order to calculate as accurately as possible the energy obtained by solar cells, we developed a simulation model that took into account all of the external restrictions and the UAV’s limits during flight. The conducted analysis made it possible to obtain information for the specific input data on whether the UAV is able to fly for 24 h in a specific flight scenario. The UAV powered by solar cells developed by us and the performed aviation missions have shown that the UAV is capable of continuous flight without the need to land.

Exploring the impact of a smart solar-powered EV charging station - A case study

The transportation sector is a significant contributor to greenhouse gas emissions, which contribute to climate change. In recent years, Electric Vehicles (EVs) have emerged as a promising solution to reduce the carbon footprint of transportation. However, one of the biggest obstacles to its widespread adoption is the lack of adequate charging infrastructure. Solar-powered EV charging stations present a promising solution to this problem, as it can provide a clean and renewable energy for charging EVs while reducing the strain on the grid during peak hours. This paper presents a detailed analysis of the technical requirements for designing and developing solar-powered EV charging stations. This includes considerations such as the location, size, and capacity of the stations, as well as the types of Evs which are designed to charge. The research also examines the economic feasibility of these stations, including the initial capital costs, operating costs, and potential revenue streams.

Design and Development of a Solar- Powered UAV Using IoT and Machine Learning

The proposed solar-powered UAV utilizes photovoltaic panels to convert solar energy into electrical power to supply the onboard electronic systems, including the propulsion system and sensors. To enhance the UAV's performance, loT technology is employed to enable the communication and coordination of the UAV with other connected devices, such as ground stations and sensors. This allows for real-time data collection and analysis, as well as improved situational awareness for the UAV. To optimize the performance of the UAV, SVM is used as a machine learning algorithm for object detection and classification. SVM has been widely used in UAVs to detect and classify objects in aerial images, and has been proven to be effective in a variety of applications. The proposed design utilizes SVM to detect and classify objects of interest, such as crops, buildings, and infrastructure, and to assist in the navigation and control of the UAV. The design and development of the proposed solar-powered UAV involves several key components, including the solar panels, propulsion system, control system, and communication system.

DoE-FEM Based Design Optimization of Hub and Spindle

A significant portion of the wheel assembly's safety is determined by the performance of the spindle and hub. In conventional designs, weight-to-strength ratio, connection configuration, etc., have barely been optimized. The purpose of this paper is to apply an optimization process through the design of experiments (DoE) concept to hub and spindle designs. To achieve better efficiency, lighter materials are targeted for use in a 3-wheel solar-powered vehicle. Using FEM analysis, both designs were statically analysed. Using Taguchi method, experiments were designed for both maximum safety and minimum weight, subjected to geometrical boundary constraints with regard to maximum safety and minimum weight. Each part was evaluated based on its impact on performance and safety. Both spindle and hub have been optimized to provide a reliable factor of safety and light weight.

Do Geopolitical Tensions and Economic Policy Uncertainties Reorient Mineral Imports in the USA? A Fat-Tailed Data Analysis Using Novel Quantile Approaches

Mineral resources are essential raw materials to generate electricity, fuel vehicles, and heat homes and workplaces. Besides, the global agenda of clean energy deployment, including solar photovoltaics (PV), wind turbines, electric vehicles (EV), and storage facilities, calls for a considerable volume of critical minerals, which elevates their respective import demands. This highly concentrated source of those minerals poses a significant concern triggered by the augmented geopolitical tensions and economic policy uncertainties. In light of this context, our objective is to estimate the response of mineral import demand to global geopolitical risk events and economic policy uncertainty covering monthly data from January 1996 to December 2020. In doing so, we apply the cross-quantilogram (CQ) and the quantile-on-quantile (QQ) regression approaches due to the fat-tailed nature of the data property. Besides, these quantile-based data analysis procedures are appropriate for non-normal data sets and show the co-movement of the variables of interest under a bi-variate modelling approach. More importantly, these two techniques also exhibit the quantile connectedness among the variables in the bearish and bullish conditions. Moreover, our findings show that mineral import demand responds negatively to the USA’s (own) and global geopolitical risk events at the high quantiles under long memory. In addition, this demand reacts positively to the USA’s (own) and global economic policy uncertainty in entire quantiles under long memory. Therefore, our policy suggestions are concerned with tackling geopolitical tensions and economic policy uncertainty by adopting pre-emptive measures within a viable institutional mechanism to continue impressive mineral trade flows.

State-of-the-Art Research on Wireless Charging of Electric Vehicles Using Solar Energy

Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) reduces the burden of EV charging on the control framework. This paper presents a state-of-the-art literature review on remote control transmission frameworks for charging the batteries of electric vehicles utilizing sun-based boards as a source of power generation. The goal of this research is to advance knowledge in the wireless power transfer (WPT) framework and explore more about solar-powered electric vehicle charging stations. To do this, a variety of solar-powered electric vehicle charging station types are thoroughly studied. Following a study of many framework elements, the types of WPT components are explored in a different section. Within the wireless power transmission framework for solar-powered electric vehicle charging, compensators and various coil structures are also investigated, along with the advantages of each coil over the others. This study also discusses the use of artificial intelligence (AI) in WPT frameworks and highlights the important aspects of developing an AI model.

Energy efficient route prediction for solar powered vehicles

Solar powered vehicles are currently being developed towards entirely self-sustaining vehicles that harness their energy directly from the sun. For such vehicles, it is important to optimise their solar exposure while driving, thereby reducing their energy consumption through fossil fuels. Research has emerged to estimate optimised routes for solar vehicles, and this paper builds on this work to expand on the parameters used to calculate the route, thereby improving the energy-harnessing quality of the route together with its overall utility for the driver. The ArcGIS tool and the open weather API are used to predict the solar potential of a vehicle by taking into account shade based on surrounding topography, vehicle type, weather, distance and time of day. The model was implemented as a user mobile application ‘Drive Solar’ that calculates the optimal route for the user based on their preferences for time and energy efficiency. The effectiveness of the prediction model was tested using a solar irradiance sensor in Dublin city. The results show that the model predicts the route with the most energy absorbed with a 51.65% accuracy and chooses the route with the most energy consumed with a 86.65% accuracy. We conclude that Drive Solar can aid in the transition to widespread use of self-sustaining solar vehicles.

Methodology to Determine the Management of Demand in Recharging Electric Vehicles in Vertically Integrated Markets Includes Photovoltaic Solar Generation

The high penetration of photovoltaic solar generation and electric vehicles in developing countries and with vertically integrated electricity markets with restrictive regulatory policies enhance demand management and the participation of prosumers in optimizing their resources. In this sense, the research presents a demand management methodology based on the prosumer model for recharging electric vehicles through optimization based on linear programming to minimize recharging costs, considering the stochasticity of the solar radiation variables, vehicular mobility patterns, consumer preferences, and optimal location of charging stations through surveys and predictive tools such as PVsyst and GAMS, in such a way that the energy demand for recharging electric vehicles is met. This way, the methodology reduces power demand peaks and mitigates the economic and technical impact on distribution networks. This case study has been modelled with real information from electric vehicles, distribution networks, and surveys in Cuenca, Ecuador.

Geopolitical risks and mineral-driven renewable energy generation in China: A decomposed analysis

Minerals are critical in manufacturing and handling renewable energy technologies, including solar photovoltaics (PV), wind turbines, electric vehicles (EVs), and power storage batteries. However, mineral mining and the supply-related risky situations stunt the mineral import flows of the renewable energy-producing countries. Given this, we scrutinize the role of the decomposed measures of geopolitical risks in the mineral import demand and this demand-driven renewable energy generation in China, covering monthly data from 1996 to 2020. We employ the quantile autoregressive distributed lag (QARDL) technique for measuring the co-integrating association among the variables across different quantiles. We find a significant effect of the decomposed measures of the geopolitical risks on the mineral import and mineral import-augmented renewable energy generation mainly at the extreme quantiles under long memory. In particular, geopolitical risks ‘threats’ and ‘narrow’ parameters negatively affect where the geopolitical risks ‘acts’ and ‘broad’ measures positively influence mineral import demand and this demand-driven renewable energy generation in China. Furthermore, we legitimize our findings across the quantile on quantile (QQ) regression technique. Moreover, our investigation outcome is beneficial for elevating mineral-driven renewable energy generation by preventing geopolitical risks in the context of China.

Lateral attitude control of solar-powered UAV with high aspect ratio

A solar-powered UAV usually has a high-aspect-ratio and flexible structural design. Its aerodynamic and mass characteristics are different from those of a conventional aircraft. This paper compares the differences in lateral aerodynamic data with those of Class I and Class III aircrafts. Based on the rigid-body dynamics theory, the effects of flexible wing deformation on the stability of the UAV are analyzed. Then, by combining the structural deformation, inertia variation and aerodynamic derivative, the solar-powered UAV's lateral stability is analyzed based on the root locus method. The variation of eigenvalues with wing deformation is also investigated. The rolling moment equation is combined with the kinematic equation to reveal the roll attitude limitation envelope constrained by the kinematic coupling and aileron effects. Taking into account the aerodynamic characteristics of the solar-powered UAV, its wing deformation and roll attitude limitation problem, this paper proposes an adaptive back-stepping control method with strong robustness to achieve the accurate control of the roll attitude of the solar-powered UAV.

Management and Modeling of a Hybrid Electric Vehicle

Abstract The use of solar electric vehicles (SEV) has recently been the subject of frantic communication by many researchers in this sector such as researcher (GASBAOUI Brahim and F. Burling). In this research, we focused on adopting other renewable energy sources, such as wind energy, to convert this energy into kinetic energy for the vehicle (EVWTG electric vehicle). The main aim of this research is to adopt the principle of a wind turbine generator in order to power an electric vehicle (EV), which relies on energy of the air currents on the surface of the vehicle and converts them into electrical energy to feed the electric motor simultaneously or storage by batteries. This system has several draw backs such as continuous energy production and controlling the speed of the wind turbine. For this, we developed a technique to ensure the production of electrical energy and its management according to the proposed system energy management technology. The operation model of the proposed EVWTG system was simulated using MATLAB, where the results obtained proved the effectiveness of the proposed control and energy management, which can be used in other practical studies in the future.

Behind-the-Meter Resources: Data-driven modeling, monitoring, and control

Behind-The-Meter (BTM) resources are distributed energy resources (DERs), such as rooftop solar photovoltaics (PVs), electric vehicles, and battery storage systems, located on the customer side of smart meters. Driven by monetary incentives, declining costs, and increasing electricity service interruptions, the penetration of BTM resources has been increasing exponentially in the past few years. For example, the small-scale BTM solar PV capacity in the United States has quickly increased from 7,642 MWac in September 2015 to 34,029 MWac in February 2022.