Solar Vehicle Research Articles

Solar Wireless Electric Vehicle Charging System

This project outlines the design and implementation of a solar-powered electric vehicle charging station that addresses the dual challenges of high gasoline prices and harmful emissions. The number of countries adopting electric vehicles continues to rise, as they not only promote environmental sustainability but also significantly lower transportation costs by substituting expensive fuels with more affordable electricity. In this context, developing an electric vehicle charging infrastructure offers an innovative and effective solution. This system enables electric vehicles to charge while in motion, eliminating the need for stationary charging stops. Powered entirely by solar energy, it requires no additional power supply. The system incorporates solar panels, batteries, transformers, regulator circuits, copper coils, AC/DC converters, Atmega328P controllers, and LCD displays. This technology demonstrates the feasibility of an on-road solar-powered wireless charging system for electric vehicles.

Comparison of Urban Environment Factors for Solar-Powered Vehicles

Abstract. Urban areas face significant environmental challenges, including high CO2 emissions largely attributed to the transportation sector. Introducing solar-powered vehicles (SPVs) presents a promising solution to reduce urban carbon footprints. However, in dense urban settings, the effectiveness of SPVs is often compromised by shading from buildings, which significantly diminishes solar power generation. This study explores the impact of shading on urban roads, aiming to identify roads with substantial shadow variations that affect solar energy harvesting. Using Geographic Information Systems (GIS) and hillshade analysis, the study examines how nearby buildings and road orientation influence shadow variability. The findings reveal considerable variability in shadow throughout the year, with significant fluctuations during transitional months like February, August, October, and November. These results highlight the importance of considering shadow patterns in urban planning and vehicle routing to optimise the use of solar energy in urban settings.

Engaging Communities in Energy Transitions: A Study on Attitudes Towards Sustainable Heating Technologies and the Role of Peer Effects in Southern Chile

This study investigates the role of peer effects in shaping the adoption of sustainable heating systems in two highly polluted communes in Southern Chile. Despite policies promoting cleaner alternatives, wood-burning stoves, a major source of particulate matter emissions, remain widespread. This research work addresses a critical gap in the literature by examining how peer influence—typically studied in relation to visible technologies like solar panels or electric vehicles—affects the adoption of less visible but essential sustainable heating technologies. The main objective of this study is to understand how peer networks can influence the attitudes of residents towards sustainable heating technologies in highly polluted urban environments. Employing a non-experimental, cross-sectional design with a sample of 244 participants, this study reveals that peer effects and health risk perception are significant predictors of positive attitudes towards sustainable heating systems. These findings contribute valuable insights for policymakers seeking to accelerate energy transitions in polluted regions.

Paving the Way for Sustainable UAVs Using Distributed Propulsion and Solar-Powered Systems

Hybrid systems offer optimal solutions for unmanned aerial platforms, showcasing their technological development in parallel and series configurations and providing alternatives for future aircraft concepts. However, the limited energetic benefit of these configurations is primarily due to their weight, constituting one of the main constraints. Solar PV technology can provide an interesting enhancement to the autonomy of these systems. However, to create efficient propulsion architectures tailored for specific missions, a flexible framework is required. This work presents a methodology to assess hybrid solar-powered UAVs in distributed propulsion configurations through a two-level modeling scheme. The first stage consists of determining operational and design constraints through parametric models that estimate the baseline energetic requirements of flight. The second phase executes a nonlinear optimization algorithm tuned to find optimal propulsion configurations in terms of the degree of hybridization, number of propellers, different wing loadings, and the setup of electric distributed propulsion (eDP) considering fuel consumption as a key metric. The results of the study indicate that solar-hybrid configurations can theoretically achieve fuel savings of up to 80% compared to conventional configurations. This leads to a significant reduction in emissions during long-endurance flights where current battery technology is not yet capable of providing sustained flight.

Mission-Based Design and Retrofit for Energy/Propulsion Systems of Solar-Powered UAVs: Integrating Propeller Slipstream Effects

Over twenty Solar-Powered Unmanned Aerial Vehicle (SPUAV) designs exist worldwide, yet few have successfully achieved uninterrupted high-altitude flight. This shortfall is attributed to several factors that cause the actual performance of SPUAV to fall short of expectations. Existing studies identify the propeller slipstream as one of these adverse factors, which leads to a decrease in the lift–drag ratio and an increase in energy consumption. However, traditional design methods for SPUAVs tend to ignore the potential adverse effects of slipstream at the top-level design phase. We find that this oversight results in a reduction in the feasible mission region of SPUAVs from 109 days to only 46 days. To address this issue, this paper presents a high-fidelity multidisciplinary design framework for the energy/propulsion systems of SPUAVs that integrates the effects of a propeller slipstream. Specifically, deep neural networks are employed to predict the lift–drag characteristics of SPUAVs under various slipstream conditions, and the energy performance is further analyzed by evaluating the time-varying state parameters throughout a day. Subsequently, the optimal solutions for the energy/propulsion systems specific to certain latitudes and dates are obtained through optimization design. The effectiveness of the proposed design framework was demonstrated on a 30-m wingspan SPUAV. The results indicated that, compared to the traditional design method, the proposed approach led to designs that more effectively accomplished closed-loop flight in designated regions and prevented the reduction of the feasible mission region. Additionally, through the targeted retrofit of the energy/propulsion systems, SPUAVs exhibited enhanced adaptability to the solar radiation characteristics of different mission points, resulting in a further expansion of the feasible mission region. Furthermore, this research also explored the variation trends in optimal solutions across different latitudes and dates and investigated the reasons and physical mechanisms behind these variations.

Two-Stage Multiple-Vector Model Predictive Control for Multiple-Phase Electric-Drive-Reconstructed Power Management for Solar-Powered Vehicles

Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the ability to control multiple targets simultaneously. In this paper, a two-stage multi-vector model predictive current control (MPCC) of a six-phase EDROC for solar-powered electric vehicles (EVs) is proposed. Firstly, the topology for the EDROC incorporating a six-phase symmetrical permanent magnet synchronous machine (PMSM) is introduced, and the operation principles of the DC charge mode, the drive mode, and, especially, the in-motion charge mode are analyzed in detail. After that, a two-stage multi-vector MPCC method is proposed by using the multi-vector MPC technique and designing a two-stage MPC structure to eliminate the regulation of the weighting factor of the MPC. Finally, the effectiveness of the proposed method is verified on a self-designed 2 kW EDROC platform.

Design strategy for the prototyping of 3D-printed continuous fiber-reinforced components for solar-powered vehicle

Continuous fiber fused filament fabrication is an advanced 3D printing method that allows designers to produce high-performing lightweight structures, leading toward innovative design philosophies and sustainable production chains. In this work, we proposed a design strategy to manufacture selected components for a solar-powered vehicle, which consists of three stages. Firstly, the prototypes’ CAD models are developed, considering their geometric and manufacturing requirements. Then, a finite element analysis is carried out to ensure optimal fiber reinforcement of prototypes’ critical regions. Lastly, the manufacturing stage involves the slicing process and optimization of the time and cost of the printed part employing Markforged® technology. Adopting the proposed methodology, the experimental campaign investigated the print quality and performance of several prototypes of each component by adjusting printing parameters.

Characteristics, Encapsulation Strategies, and Applications of Al and Its Alloy Phase Change Materials for Thermal Energy Storage: A Comprehensive Review

AbstractAmong metal‐based phase change materials (PCMs), Al and its alloys have garnered significant attention due to their high latent heat and high thermal conductivity. However, challenges such as leakage, corrosion, and oxidation have limited their widespread application. Numerous researches have demonstrated that encapsulating Al and its alloy PCMs is one effective way to address these problems. This review provides a comprehensive overview of the characteristics, encapsulation strategies, and applications of Al and its alloy PCMs. First, the advantages and thermal properties of Al and its alloy PCMs are introduced, and their problems are then discussed. Subsequently, various encapsulation strategies that are expected to solve the above problems are summarized and compared. Additionally, the applications of Al and its alloy PCMs in solar thermal energy storage, catalysis, and electric vehicles are reviewed. Finally, current challenges, potential solutions, and the key direct of future study are presented.

Intelligent solar light electric vehicle moving in hot regions under a hybrid power source strategy

Solar Light Electric Vehicles (ISLEV) have recently gained increasing attention as a promising solution for sustainable transportation. In hot regions, such as the BECHAR region in southwest Algeria, where temperatures can exceed 50 °C, the challenge of maintaining high performance standards while coping with challenging environmental conditions is critical. To address this challenge, this study proposes an intelligent hybrid power source strategy comprising SUN Power solar PV and lithium-ion battery supercapacitors. To optimize the vehicle's performance, a feedforward neural network FF-NN is employed, which regulates the power flow between the hybrid sources. The proposed system's efficiency and effectiveness are assessed through simulations, which reveal its robustness in coping with challenging environmental conditions while maintaining high performance standards. The results show that the proposed system holds con-siderable potential for sustainable and efficient transportation in hot regions and can have broad applications in diverse sectors. Future industrial vehicle designs must incorporate the choice of hybrid power management into their planning process to ensure the efficient use of energy and optimize the vehicle's performance.

Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data

Green technologies such as solar photovoltaic systems and electric vehicles play a fundamental role in the global decarbonization effort. To enable their diffusion, electricity distribution networks need to be upgraded, which is a complex and expensive endeavor. However, utilities face budgetary constraints and seek to reduce planning uncertainty. Here, we utilize 7,527 real-world grid topologies and land cover data to develop a model for estimating conductor and capital investment needs for electrifying a specific area. Our work yields three main contributions: First, we demonstrate the important role of land cover data in power line planning. We show that, for medium and large networks, distinct methodologies are needed due to the significant impact of land cover, particularly buildings and roads. Second, we introduce a parsimonious model of power line length and identify the number of consumption points as the primary determinant of network investment costs. Third, we present a cost assessment model tailored for regulators and investors, offering valuable insights for network planning, policymaking, due diligence, and research. Our work highlights the importance of combining land cover data and operations research algorithms in distribution network planning and provides policymakers with a tool to ensure cost-efficient network expansion.

Design analysis and techno-economic assessment of a photovoltaic-fed electric vehicle charging station at Dhaka-Mawa expressway in Bangladesh

Electric vehicles are crucial for sustainable transport and energy solutions, particularly in developing countries like Bangladesh where their popularity is rising. This study primarily focuses on the techno-economic design of a 300 kWp solar photovoltaic-powered electric vehicle charging station along the Dhaka-Mawa Expressway in Bangladesh, capable of charging 20 electric vehicles simultaneously. The design utilizes the commercially available software package PVsyst 7.2 to ensure the feasibility and efficiency of the charging infrastructure. The use of solar photovoltaics for electric vehicle charging, compared to traditional grid-based methods, offers substantial environmental benefits, including significant reductions in carbon emissions. This shift is driven by decreasing costs, improved module efficiencies, and increased environmental awareness. The estimated levelized cost of energy is calculated at 7.1556 BDT/kWh (BDT is Bangladeshi Taka), with an annual energy generation cost of 1436285.32 BDT. Over a projected lifespan of 25 years, the system is expected to replace 8065 tons of CO2 emissions with its own emissions totaling 537.56 tons, resulting in a net decrease of 6460.2 tons of CO2. This approach not only aligns with Bangladesh’s emission reduction goals but also exemplifies the potential for solar photovoltaic systems to enhance sustainability in transportation. It also emphasizes the importance of integrating renewable energy sources into the electric vehicle-based infrastructure to achieve true sustainability and supports the country’s commitment to combating climate change through technological innovation.

Design and Performance Evaluation of Multilevel Inverter for Solar Energy Systems and Electric Vehicle Charging with Multi Output Active Clamp Forward Converter

Design and Performance Evaluation of Multilevel Inverter for Solar Energy Systems and Electric Vehicle Charging with Multi Output Active Clamp Forward Converter

Energy-saving path planning navigation for solar-powered vehicles considering shadows

Globally, the adverse climate effects caused by greenhouse gas emissions are becoming increasingly apparent, and solutions to increase the use of eco-friendly transportation methods are urgently needed. Introducing solar-powered vehicles (SPVs), which are cars integrated with solar panels capable of generating power, presents a promising solution to reduce urban carbon footprints. However, the low adoption rate of SPVs implies that the benefits—such as environmental friendliness and ability to charge while driving—need to be more palpably experienced by consumers. To address this aspect, in this study, we aimed to develop a navigation system algorithm that guides users along routes that optimize energy consumption and solar energy production from the starting point to the destination. This was done with the objective of providing more tangible benefits from using SPVs. The study focused on the high-traffic urban center of Seoul, where determining solar power availability for a moving SPV is challenging, given the presence of shadows cast by roadside features such as buildings and trees. To achieve this, panoramic images from Google Street View were collected at 10 m intervals from all roads within the research area. From these images, sky and non-sky elements were separated. Subsequently, a hemispherical map was constructed and superimposed with the sun's path. The presence of shadows was determined by assessing whether the sun's path was obstructed by non-sky elements; if the path was unimpeded in the sky, no shadow was recorded. The shadow data obtained at each spot were efficiently stored in a database for quick retrieval and application based on specific locations and departure times. Using this shadow information, the navigation algorithm calculates power generation along a given route and considers the energy consumption of the SPV. Analysis led to the identification of an energy-saving route, which enabled the achievement of energy conservation and CO2 reduction benefits. Furthermore, a comprehensive sensitivity analysis was conducted to examine the impact of four critical parameters—module efficiency, solar panel area, vehicle speed, and departure time—on route selection and net energy consumption. The energy-saving path planning algorithm enhances the economic feasibility of solar charging for SPVs during travel; thus, this study can contribute significantly to the widespread adoption of SPVs, which play a definitive role in reducing transportation's carbon footprint.

Multi-disciplinary Analysis of Solar-Powered Unmanned Aerial Vehicles for Extended Endurance

Multi-disciplinary Analysis of Solar-Powered Unmanned Aerial Vehicles for Extended Endurance

Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station

The main objective of this study is to develop ANN-based predictive models for short-term forecasting of solar PV power output and battery state of charge. The 3Ds energy model that integrates Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the shift towards sustainable energy sources is used for this electric vehicle project. The experimental set up includes solar PV panels, a solar inverter, a battery inverter, and a battery bank. Additionally, smart meters were installed to collect real-time performance data from the solar PV-powered electric vehicle (EV) charging station. The weather and real-time system performance data were used to develop short term forecasting models based on Artificial Neural Networks (ANN) and the Levenberg-Marquardt method, to predict the performance of the system (power output and state of the charge) ahead. The R values for the prediction models of solar photovoltaic (PV) specific power and battery state of charge fall within the range of 0.9957–0.9969 and 0.9990 to 0.9996, respectively. Furthermore, the mean squared errors (MSEs) for the artificial neural network (ANN) models pertaining to solar photovoltaic (PV) power output and battery state of charge exhibit a variety of values. Specifically, the MSEs for solar PV power output range from 1.242 x 10^-4 to 1.579 x 10^-4, while the MSEs for battery status of charge range from 0.1889 to 0.3402. Artificial neural network (ANN) models possess considerable promise for practical implementations as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios. The level of accuracy and short-term predictive models of the solar PV powered EV charging station are very important for achieving a balance between the supply of solar photovoltaic (PV) system and the demand for electric vehicles (EVs). Predictive models will help build complex control mechanisms for controlling and optimizing solar PV-powered charging stations, supervising their operation and maintenance, and simplifying renewable energy pre-purchase. Future works will include the development of an energy management system to improve the efficiency of the solar stations charging the EVs, the establishment of blockchain networks for both the solar PV system and battery bank, and the integration of cybersecurity protocols for the charging station.

Virtual Development of a Challenger type, single-seater vehicle for the World Solar Challenge Competition

Abstract The paper presents the evaluation of the feasibility of a single-seat solar prototype vehicle for the World Solar Challenge competition, by estimating its autonomy. The research carried out contains several stages of study, including the development of mathematical models that are intended to be accurate enough to provide realistic predictions on the functional behaviour of the battery pack, so that it can be determined whether the designed vehicle will be able to complete the stages of the proposed competition. The paper also presents the CFD modeling approach, and the results obtained after simulating different body shapes advancements. In addition, the functional modeling approach is presented to evaluate the vehicle battery range, including the possibility to use different designs and types for the battery pack and to verify if the designed vehicle can achieve the race finish line.

Design of wireless charging system for E-Vehicle

To address the dual problems of fuel reliance and air pollution, this study describes the design of a wireless ground to vehicle charging system powered by solar energy and specifically designed for electric vehicle (EV) charging stations. As the number of electric vehicles on the road steadily rises, they present a viable way to cut travel expenses by switching from conventional fuel to electricity, which is a more economical and sustainable option. With the introduction of a wireless EV charging system, this creative solution does away with the need for external power sources and permits continuous charging without interfering with driving. Utilizing solar power, the charging system incorporates solar panels, batteries, circuit regulators, boost converters, receiver and transmitter copper coils, AC/DC converters, microcontrollers (such as ATmega), LCD screens, and circuit regulators. This study describes a technique that shows that charging electric cars while driving is feasible and eliminates the need to stop. This technology for wireless solar electric vehicle charging presents a forward-thinking approach to sustainable mobility by providing a workable solution that can be easily included in the road infrastructure. For the wireless charging, in addition, various coil designs are suggested.

Heavy-duty vehicles with solar panels as a regenerative power source

Abstract Road transportation represents more than a third of all CO2-producing sources on Earth, being the main producer of greenhouse gas (GHG) emissions. Nowadays, continuous development of technology has reduced fossil-fuel consumption by integrating clean energy sources as a range extender. Solar energy has the largest potential of all renewable energy sources worldwide. The photovoltaic and energy storage research fields have grown exponentially during the last decade and, forced by the current energy crisis, had a great contribution to the hybrid solar vehicles (HSV) industry. Vehicle Integrated Photovoltaics (VIPV) comply with requirements on vehicles aesthetics, and they convert solar energy into electric energy to supply the low-voltage and high-voltage battery packs onboard HSV. Heavy-load road transportation by commercial vehicles equipped with PV-integrated modules or retrofit installation will contribute to global decarbonization. A study is done on a configuration of a heavy-duty vehicle with its cuboid cargo body covered with solar panels and a regulated driving route during different weather conditions [3]. The energy management system is designed to maximize solar energy conversion in order to reduce fuel consumption and extend the vehicle’s total driving distance.

An Integration of Adaptive Control Techniques with Portable Solar-Powered EV Charging with Particle Swarm Optimization (PSO)

Abstract: This paper presents a novel approach utilizing Particle Swarm Optimization (PSO) for enhancing the efficiency of solar-powered electric vehicle (EV) charging systems. With the increasing adoption of EVs and the imperative to transition towards renewable energy sources, optimizing the utilization of solar power for charging becomes crucial. The proposed system integrates photovoltaic (PV) panels to harness solar energy, providing a sustainable and renewable power source for EV charging. The key innovation lies in the application of PSO, a nature-inspired optimization algorithm, to dynamically adjust charging parameters based on real-time environmental conditions, grid electricity prices, and EV battery requirements. PSO offers a robust and efficient method for solving optimization problems by simulating the behavior of swarms in nature. In the context of solar-powered EV charging, the PSO algorithm iteratively adjusts charging rates and schedules to maximize solar energy utilization, minimize charging costs, and ensure optimal battery health. The integration of PSO optimization with solarpowered EV charging represents a promising step towards achieving sustainable and intelligent transportation solutions. Future research directions may include further optimization refinements, scalability studies, and real-world deployment to validate the practicality and effectiveness of the proposed approach in diverse environments and usage scenarios.

Economic Analysis and Design of Sustainable Solar Electric Vehicle Carport at Applied Science Private University in Jordan

Electrical vehicles are finding wide acceptance in the Jordanian transportation market; this has caused an accelerating shift in the emissions of greenhouse gases from the direct burning of fossil fuels consumed by the transportation sector towards the power generation sector. On the other hand, as electric vehicles gain more popularity, an extra load is added to the electrical power generation systems, raising essential concerns such as the capability of the power network to support this massive extra load and the increased emission of greenhouse gases caused by power plants. Studies show that Jordan’s weather is known for being bright, sunny, and very suitable for the generation of electric power from solar energy sources. Therefore, with an infrastructure that can support convenient off-grid charging, a huge burden will be taken off the national grid and the environment. Therefore, this work proposes a basic design for an off-grid PV-covered carport and presents a study of the economics and effectiveness of using such a system to charge electric vehicles owned by the students and employees of the Applied Science Private University. The study is based on actual solar irradiance data collected on-site during university working hours (8 a.m.–5 p.m.) to allow students and employees to charge their electric vehicles from an off-grid carport system while on campus. Space limitations for carport design, initial design cost, return on investment, and annual electricity consumption are discussed to demonstrate the benefits of such a system for both the consumer (convenience and low charging cost) and the power company provider (less load to maintain).