Solar Charging Station Research Articles

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

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.

Strategic deployment of GIS-optimized solar charging stations for electric vehicles: A multi-criteria decision-making approach

Climate change and the rise in carbon dioxide levels due to gasoline vehicles are global challenges that require innovative and sustainable solutions; this study presents an innovative strategy to promote electric vehicles (EVs) adoption through the establishment of solar-powered charging stations. Utilizing ArcGIS10.8.2 software, a comprehensive analysis was conducted to identify optimal locations for these stations, considering technical, economic, environmental, and geological data. This research, unlike other past research, leverages public spaces such as gas stations, shopping centers, and parking lots for station construction, minimizing operational costs and fostering a sustainable infrastructure. This approach applies not only to the study area of this research, which is Khuzestan province, but can also be extended to other regions and serves as a model for reducing CO2 emissions in the transportation sector worldwide. This research evaluates the location for establishing electric vehicle charging stations using solar energy innovatively, from both technical and operational perspectives. By using the systematic and new method presented in this research, it is possible to identify the highest potential for the construction of electric car charging stations that simultaneously use solar energy all over the world, and the results of applying this new method in the studied area showed that 90% of the cities within this province have the potential to establish charging stations for electric vehicles utilizing sunlight. Specifically, Mahshahr County in this province can supply 90.55% of the required energy for charging stations through solar energy. Operationally, 70% of the cities in the region possess such potential. The study demonstrated that this area has the capacity to convert 11% of vehicles to electric cars by 2040 and can reduce CO2 emissions by more than 30 tons.

Intelligent hybrid deep learning models for enhanced shipboard solar irradiance prediction and charging station

Electrifying marine routes with solar energy significantly reduces ocean carbon emissions, playing a vital role in climate regulation. Solar irradiance forecasting ensures reliable power despite unpredictable sea weather, necessitating innovative model development. This research presents a forecasting model designed for the optimal placement of solar charging stations, providing hour-ahead solar irradiance predictions for onboard solar vessels. India (Jawaharlal Nehru Port Trust)– United States (Port of New York and New Jersey) sea route is selected to test our proposed forecasting model, as it aligns with busy shipping operations and long-distance trade requirements for electrification. Two distinct procedures by two case studies are used to select optimum data to predict the suitable locations for solar charging stations along the navigation route. In the first case study (Medium data analysis – MDA), the data are selected based on a statistical analysis of twenty years of hourly solar irradiance data from 201 locations. In the second case study (Large data analysis – LDA), two years of seasonal data from 201 locations are chosen and combined to form a large amount of hourly data. For both case studies, a hybrid solar irradiance forecasting model (ARIMA – Bi – LSTM) is developed by integrating the deep learning model with the time series model and fine-tuning them using optimization algorithms (PSO and GA). In MDA, the PSO optimization achieves mean absolute error (MAE) values of 0.32, 0.85, 1.83, 1.40, and 1.33 W/m2 for locations 1, 2, 3, 4, and 5, respectively, significantly outperforming the GA, which has MAE values of 1.36, 1.65, 3.25, 0.92, and 1.41 W/m2 for the same locations. In LDA, the PSO model achieves MAE values of 0.79, 0.91, 0.28, and 0.39 W/m2 for winter, rain, summer, and spring, respectively, also outperforming the GA, which has MAE values of 1.54, 2.17, 0.99, and 1.46 W/m2 for the same seasons. Comparatively, the LDA reinforces its best alignment to predict optimal charging station locations.

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.

A Sustainable Solution for Urban Transport Using Photovoltaic Electric Vehicle Charging Stations: A Case Study of the City of Hail in Saudi Arabia

As the global shift toward sustainable transportation gains momentum, the integration of electric vehicles (EVs) becomes imperative, necessitating a robust and environmentally friendly charging infrastructure. Leveraging the abundant solar potential in the region, this study examines the technical, economic, and environmental feasibility of deploying photovoltaic electric vehicle charging stations (PV-EVCSs) in Hail City, Saudi Arabia, as a case study. This study examines factors such as the energy demand, grid integration, and user accessibility, aiming to address the challenges and opportunities presented by the urban fabric. The proposed solar charging station network seeks to catalyze a paradigm shift toward a cleaner and more sustainable transportation ecosystem, embodying a forward-thinking approach to meeting the evolving needs of urban mobility in the 21st century. The analysis encompasses many scenarios, encompassing a range of car battery sizes, charger powers, and car slots per station. Zone 4 is identified as the most crucial area, where seven charging stations are needed to fulfill the expected demand in the absence of any private charging alternatives. The economic evaluation of the 1047.35 kWp PV system reveals an estimated conventional payback time of 11.69 years, accompanied by a return on assets of 10.17%. The system generates accumulated cash flows amounting to SR 7,169,294.62 over 30 years, while the estimated operational and maintenance expenses are predicted to be SR 50,000 per year. The overall investment cost for the solar PV and EV charging stations is SR 4,487,982. This cost is offset by the yearly electricity savings from solar and grid sources, which can reach up to SR 396,465.26 by year 30. This work presents a detailed plan for the future of sustainable transport. It combines technical, environmental, and economic aspects to promote a cleaner and more sustainable urban mobility system.

Hybrid Charging Station

Abstract: In an attempt to decarbonize the transportation sector, among many countries, is option for e-mobility as an optimal solution, to create a greener, cleaner, and more affordable future for everyone. However, it is missing a crucial prerequisite, which is a strong EV charging infrastructure. The various EVCS types, technologies, techniques, and equipment. It also includes the design of a charging station for small EVs for on campus use, with a solar energy system input. Next, a mechanical 3D design for the final product. As well as a proof-of- concept implementation. Lastly, some conclusions, limitations, and recommendations for further research. A solar charging station is meant so that vehicles is fully charged and is environmentally safe. this technique transforms solar power to electricity and stores it in an battery bank. If electric vehicles must be truly imperishable, it's essential to charge them from sustainable sources of electricity, like solar or wind energy. In this paper, the solar charging station that gives the electricity to charge the battery. The charging station has integrated battery storage that allows for off-grid operation. The DC charging uses the DC power from the photovoltaic panels directly for charging the vehicles battery without the utilization of an AC charging adapter.

Collective impact of radiation on tetra hybrid nanofluid flow over a stretching sheet: Solar-powered charging station

An enclosure with thermal radiation is widely engaged in the current decade due to the role of solar energy applications like photovoltaic panels, renewable power, solar light poles, oil recovery, solar vehicles, and solar pumps for water extraction. After getting motivated by these uses, the current scientific research is designed to address the energy crisis issue by capturing the high-level performance in solar-powered charging stations and to reveal the implications of thermal conduction together with the radiation influence on the flow of tetra hybrid nanofluid in a porous medium. This recent physical advancement is witnessed by employing conservation laws in the mathematical representation of nonlinear partial differential equations. The governed equations are turned into nondimensional ordinary differential equations by making use of the ideally suited similarity variables and cracked numerically. The scientific outcomes acquired in a specific scenario coincide with the findings retrieved from the earlier literature in order to standardize the deployed numerical approach. Moreover, the measurement of engineering concern is forecasted through the execution of both the multiple linear regression and multi-layer perceptron concerning energy in solar-powered charging stations. The current findings exhibit that the ( Ag ‐ Ti O 2 ‐ Cu ‐ A l 2 O 3 / EG ) provides a superior heat source than ( Si O 2 ‐ Ti O 2 ‐ Cu ‐ A l 2 O 3 / EG ) tetra hybrid nanofluid flow over an expandable sheet in solar power charging station. The coefficient of determination values of multiple linear regression and multi-layer perceptron models were developed to estimate the skin friction coefficient value for case 1 as 94.74 and 99.21 % , whereas these models for case 2 were obtained as 94.84 and 98.35 % , respectively. It shows that the proposed multi-layer perceptron model can forecast with higher precision and is a powerful engineering tool that can be effectively employed in tetra-hybrid nanofluid.

Design, simulation and analysis of solar electric vehicle charging station

Design, simulation and analysis of solar electric vehicle charging station

Hybrid Wind/PV E-Bike Charging Station: Comparison of Onshore and Offshore Systems

The concept behind this research article is advancement towards utilizing renewable energy sources of wind–solar to generate electrical energy for E-bike (electric bike) charging stations. To optimize the design and operation control of the wind–solar E-bike charging station system, the development of modelling this hybrid power generation system, consisting of solar and wind energy combined with battery storage, is proposed and will be studied in this paper. A university campus setting is utilized for the case study by comparing offshore (Huangdao) and onshore (Laoshan) sites. The proposed research will focus on annual energy production (AEP) and system cost analysis. The proposed work’s main objectives are to analyze the wind/solar properties of the installation’s location using the last 20 years’ data, calculate the AEP for wind turbines and solar PV, and estimate how many E-bikes can be charged day/year with reliable operation. We have calculated that the hybrid power available is 27.08 kWh/day offshore and 22 kWh/day onshore. This research study concludes that on average, based on AEP, in the case of offshore, 5110 E-bikes can be charged per year and in the case of onshore, 4015 E-bikes can be charged per year. We have also calculated the COE (cost of energy) for 20 years for the proposed project, which is $0.62/kWh onshore and $0.46/kWh offshore.

Grid-Sim: Simulating Electric Fleet Charging with Renewable Generation and Battery Storage

The inevitable electrification of the sub-Saharan African paratransit system poses substantial threats to an already crippled electricity supply network. The integration of any electric vehicle fleet in this region will require in-depth analyses and understanding of the grid impact due to charging. This allows informative decisions for sufficient planning to be made for the required network infrastructure or the implementation of applicable ‘load-shifting’ techniques. This paper presents Grid-Sim, a software tool that enables comprehensive analysis of the grid impact implications of electrifying vehicle fleets. Grid-Sim is applied to assess the load profiles, energy demand, load-shifting techniques, and associated emissions for two charging stations serving an electrified minibus taxi fleet of 202 vehicles in Johannesburg, South Africa. It is found that the current operation patterns result in a peak grid power draw of 12 kW/taxi, grid-drawn energy of 87.4 kWh/taxi/day, and, subsequently, 93 kg CO2/taxi/day of emissions. However, when using the built-in option of including external batteries and a solar charging station, the average peak power draw reduces by 66%, and both grid-drawn energy and emissions reduce by 58%.

A CVaR-based stochastic framework for storm-resilient grid, including bus charging stations

This paper proposes a two-stage stochastic framework for improving distribution system resilience against storms, in which the uncertainties associated with load demands, solar irradiance after a storm event, and maximum gust wind speed are considered. In the first stage, the available idle electric buses (EBs) are optimally allocated to the charging stations prior to storm arrival. In the second stage, critical loads are restored through island formation after the storm. In this framework, the solar-powered charging stations of EBs and the distributed generators (DGs) are part of the electric energy resources. The solar charging stations contribute to supplying the critical loads in two ways: 1- The electricity generation through rooftop-installed photovoltaic modules 2- By discharging the batteries of the pre-allocated EBs. The objective of this optimization model is to minimize the expected priority-weighted curtailed energy. At the same time, the risk imposed by the uncertain parameters is taken into account through the conditional value-at-risk (CVaR) index. The proposed framework is expressed in terms of a stochastic mixed-integer linear programming (MILP) optimization problem. This framework is implemented on the 33-node distribution test system, and its superior resilient performance is demonstrated through four case studies.

A robust BCNMCC based variance smoothing approach for SPV based grid integrated EV battery charging system

The demand for good quality charging infrastructure is increasing rapidly as the number of electric vehicles (EVs) are increasing day by day. Nowadays, the contribution of non-conventional and distributed energy sources is rising. Consequently, the solar energy-based charging station is anticipated to grow. Therefore, this paper presents a multifunctional solar photovoltaic (SPV) based single-phase grid-integrated charging station reinforced with an EV battery energy storage (EVBES). The support of EVBES also improves the viability and reliability of the charging station. The voltage source converter (VSC) facilitates harmonic mitigation, reactive power compensation (RPC) and power factor correction (PFC), under different practical situations besides providing the charging to the EVBES. The robust bias-compensated normalized maximum correntropy criterion (BCNMCC) based adaptive filtering approach is utilized to compute the real power component of the nonlinear load current. It minimizes the mean square error and improves dynamic response in varying ambiance situations. The performance of the charging system is observed in the MATLAB-Simulink platform. An experimental prototype of proposed model is developed and tested at various practical situations. The recorded results demonstrate satisfactory operation of charging station in laboratory environment.

Electric Vehicle Solar Charging Station Siting Study Based on GIS and Multi-Criteria Decision-Making: A Case Study of China

Electric vehicles (EVs) are one of the most practical solutions to the energy issue and environmental pollution. In recent years, EVs have developed rapidly, but are still limited by charging problems. The emergence of photovoltaic charging stations can solve the environmental pollution and charging problems. The location of charging stations is critical in the life cycle of electric vehicles. In this paper, a multiple-criteria decision-making (MCDM) method based on Geographic Information Technology (GIS) for optimal site selection is proposed. First, based on literature reading and expert interviews, a site selection index system was identified, including four aspects with a total of ten sub-criteria. Secondly, a spatial database of relevant evaluation criteria was established using GIS, and preliminary analysis was conducted. Then, the fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory method) is applied for assigning the criteria weights. Then, potential sites are ranked using the fuzzy MULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method. Then, the model was validated by siting the electric vehicle PV charging stations in Qingdao, and eight stations were identified in the preliminary selection stage, and the most suitable locations were finally selected through the MCDM stage. Finally, the reliability and validity of the model were further verified by comparative analysis and dual sensitivity analysis.

Designing a Solar PV-Battery based on Electric Vehicle Charging Station

Increasing transport demand necessitates higher oil consumption, resulting in an increase in carbon dioxide (CO2) emissions, which is a major cause of air pollution. The use of electric vehicles (EVs) is becoming more common around the world. Recent advancements in lithium-ion battery technology have increased the improvement of EVs. In this work, a solar photovoltaic (PV) battery-based EV charging station is designed. Meanwhile, the overall system comprises a battery energy storage system (BESS), solar PV module, grid and EV charging station. Thus, the primary source for the charging station is the PV source but due to less power during the night, we included battery storage as a backup. Grid source is also recommendable for an uninterruptable power supply. An artificial neural network strategy is developed in MATLAB/Simulink for proper power management of the solar PV-battery based EV charging station connected to the AC grid. Moreover, by employing an adaptive neuro-fuzzy inference system (ANFIS) and PI controller-based MPPT, the grid voltage and current, real/reactive grid power and the maximum output power are obtained. The overall system is evaluated under different scenarios of irradiance level and temperature with a state of charge (SOC) greater than 10 % for simulation purposes. The result shows that during the night hour due to less power from the PV source, an artificial neural network begins to regulate the grid power so that it supplies power to the stationary storage and EV battery.

Design and Development of Smart Solar Charging Station

Design and Development of Smart Solar Charging Station

An approach for selecting optimal locations for electric vehicle solar charging stations

AbstractElectric vehicles (EVs) are seen as a solution to reduce transport‐related greenhouse gas emissions. A major obstacle to wider adoption is the insufficient amount of charging stations. Furthermore, supplying charging stations with renewable energy is still in its infancy. The selection of optimal locations for charging stations is important to best serve the users and maximise the possibilities of renewable energy use. Given this background, this study developed an approach for Solar‐supplied Electric Vehicle Charging Station (EVCS) location selection by combining EVCS and solar farm site selection studies using Geographical Information System (GIS) and Analytic Hierarchy Process (AHP). The study determined the most important criteria for site selection based on previous solar and EVCS site selection studies and expert opinions. The 10 most important criteria according to the survey results were: availability of power, solar energy potential, solar panel installation cost, number of EVs, operation and management costs, land cost, distance from roads/highways, distance from current EVCSs, industrial capability of installation and distance to high population density centres. The importance weights of these criteria were assigned using AHP method. The findings are expected to benefit urban planners, decision‐makers, and researchers designing solar‐supplied EV charging infrastructure.

Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy

Charging electric vehicles (EV) by photovoltaics (PV) contributes to achieving carbon neutrality, but puts pressure on urban renewal, e.g., large investments in distribution grid upgrade and energy storage (ES). To solve this problem, we proposed a charging system aiming at providing intermittent but free solar charging service for private EV drivers to cover their daily intra-urban transportation demand. It is a battery-free direct-current (DC) microgrid with a distributed charging strategy, taking variable DC bus voltage as a control signal. The system was optimized and tested by simulation on an office building with 80 private EVs. We performed a system performance comparison, when achieving the same guarantee level of EV use with the parking shed fully cover by PV. A 10-kW charger (DC Level 1) with the strategy can take the role of a 3.5-kW charger (AC Level 2) with ES (26.0 kWh/parking space) to coordinate PV generation and charging load. Furthermore, connecting the system with the adjacent building can increase the annual solar self-consumption rate from 33.3% to 67.9%, and avoid the distribution grid upgrade (average 5.6 kVA/charger). The discounted payback time of this building-connected system decreases from 9.4 to 4.5 years, compared with a conventional solar charging station.

Wireless Solar Charging Station and Battery Management using Arduino

Electric-powered vehicles will help reduce greenhouse gas emissions and increase fuel prices. The main purpose of wireless transmission in electric vehicles is to transfer power over a small distance. The wireless power transmission system consists of a transmitter and receiver part that is separated by a small distance. Wireless transmission technology uses a flexible electromagnetic field. This electric field is created in a free environment that carries a fixed amount of money that creates a magnetic field around it and this field contains energy in it and the EMF is generated between the coils and transmitted to the receiver. BMS is a battery management system. In EV vehicles we use two batteries such as master and slave. The first preference is given to the master battery in BMS. If the master battery charge comes down automatically the relay will switch from battery

Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators

Even though generating electricity from Renewable Energy (RE) and electrification of transportation with Electric Vehicles (EVs) can reduce climate change impacts, uncertainties of the RE and charged demand of EVs are significant challenges for energy management in power systems. To deal with this problem, this paper proposes an optimal energy management method using the Virtual Power Plant (VPP) concept for the power system considering solar PhotoVoltaics (PVs) and Electric Vehicle Charging Stations (EVCS). The Differential Evolution (DE) algorithm is applied to manage energy in the power system to minimize the operation cost of generators and degradation costs in Energy Storage Systems (ESS) and generators. The effectiveness of the proposed approach is examined and tested on the IEEE 24 bus Reliability Test System (RTS 24) using the MATPOWER tool on the MATLAB program for calculating Optimal Power Flow (OPF). In this study, two situations before and after applying the proposed method are considered. The simulation results demonstrate that a branch constraint violation occurs before using optimal energy management using the VPP concept. In order to solve this issue, the DE algorithm for optimal energy management using the VPP concept is applied by dividing the studied case into two subcases as follows. For the first subcase, two objectives consisting of the minimization of the generator operation cost and the minimization of the battery degradation cost in ESS are considered. In the second case, three objectives comprising the two mentioned objectives with the minimization of generator degradation cost are considered. The results demonstrate that branch constraint violations can be avoided by applying optimal energy management using the VPP concept. This study also suggests considering the generator degradation cost in the objective function, which can minimize the total costs by 7.06% per day compared with the total cost of the first case.