Photovoltaic Electric Vehicles Research Articles

Design, Control, and Evaluation of a Photovoltaic Snow Removal Strategy Based on a Bidirectional DC-DC Converter for Photovoltaic–Electric Vehicle Application

A novel self-heating technique is proposed to clear snow from photovoltaic panels as a solution to the issue of winter snow accumulation in photovoltaic (PV) power plants. This approach aims to address the shortcomings of existing methods. It reduces PV cell wear, resource loss, and safety risks, without the need for additional devices. A self-heating current is applied to the solar panel to melt the snow covering its surface, which is then allowed to slide off the panel due to gravity. The proposed system consists of a bidirectional DC-DC converter, which removes the snow cover by heating the solar PV modules using electricity from the grid or electric vehicle (EV) batteries. It also charges the EV battery pack and/or supplies the DC bus when no EV is plugged into the charging station. For each mode of operation, a current-controlled system was implemented using a PI controller and a model predictive controller (MPC). The MPC approach achieved a faster rise time, shorter settling time, very low current ripples, and high stability for the proposed system. Specifically, the settling time decreased from 9 ms and 155 ms when using the PI controller at 20 µs and 35 µs with the MPC controller for both the buck and boost modes, respectively.

Design and Implementation of Photovoltaic Electric Vehicle Charging Stations: A Case Study at Universidad Tecnológica de Pereira

As the global transportation sector transitions towards sustainability, the need for renewable energy- powered electric vehicle (EV) charging infrastructure becomes increasingly evident. This paper presents a case study on the design and implementation of a photovoltaic electric vehicle charging stations at the Universidad Tecnológica de Pereira (Colombia). The study outlines the design process, technical specifications, and implementation strategies for a photovoltaic (PV) charging station, emphasizing key considerations such as location selection, solar panel configuration, and necessary equipment. Additionally, the paper evaluates the performance and effectiveness of the PV charging station in supplying the demand for EV charging services on campus. This case study provides valuable insights into the feasibility and scalability of PV EV charging infrastructure in educational institutions.

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.

Assessing Criteria for Photovoltaic Electric Vehicle Charging Stations in Malaysia: Analytic Hierarchy Process and Content Validity Index Analysis

Integrating electric vehicle charging stations (EVCSs) with renewable energy sources (RES) further enhances these advantages, addressing range anxiety among EV users. Allocating suitable locations for Photovoltaic Electric Vehicle Charging Stations (PEVCS) is crucial in Malaysia to optimize their utility and accessibility. This study employs a dual methodology combining the Content Validity Index (CVI) and the Analytic Hierarchy Process (AHP) to validate instruments and analyze expert judgment. Through a comprehensive literature review and need analysis, the study identifies and validates main criteria and sub-criteria influencing the placement of PEVCS in Malaysia. The CVI is used to develop an AHP questionnaire, ensuring the robustness of the instrument. The findings reveal six main criteria and twelve sub-criteria crucial for determining ideal PEVCS locations. Economic factors emerge as the most influential main criterion, with construction costs ranking highest among sub-criteria. Utilizing AHP calculations, these criteria weights are integrated into GIS modelling using ArcGIS software to identify optimal PEVCS locations. This study offers significant advantages to industry leaders, policymakers, and stakeholders in effectively allocating suitable locations for PEVCS in Malaysia. By enhancing environmental sustainability and addressing logistical challenges, this approach contributes to the advancement of EV infrastructure and adoption in Malaysia.

Charging Ahead: Statistics on Electric Vehicle Charging Station Allocation and Uptake Trends in Malaysia

The emergence of electric vehicles is attributable to the accessibility of charging stations, which is essential in reducing EV drivers' range anxiety. EV batteries need to recharge after a certain number of miles driven. As a result, for EVs to be widely deployed, a sustainable charging station must be constructed. The National Electric Mobility Blueprint states that Malaysia's primary goal is for the nation to become the marketing hub for the EV industry by 2030. By 2030, Malaysia reportedly expects to have 125,000 electric vehicle charging stations. EVCS are available in many types, each with different charging capabilities and speeds. The most popular EVCS are slow charging stations, fast charging stations, battery swap charging stations, and wireless charging stations. The type of EVCS is discussed in this study, particularly from the perspectives of Malaysia. The mechanisms, advantages, disadvantages, and associated issues of these EVCS are thoroughly discussed. Furthermore, to reduce range anxiety among EV users and enhance EV adoptions in Malaysia, several criteria are considered in determining the suitable location for Photovoltaic Electric Vehicle Charging Stations. For future studies, all the selected criteria will be calculated using Multi-Criteria Decision-Making methods.

A Need Analysis of the Criteria Involved in Determining Suitable Locations for Photovoltaic Electric Vehicle Charging Stations in Malaysia

The main concern is a lack of scientific planning for photovoltaic electric vehicle charging stations (PEVCS) that considers numerous main criteria and sub-criteria. PEVCS should be strategically positioned in an appropriate and ideal location to ensure that electric vehicle (EV) users may reach the stations within their driving range. While the adoption of solar is still minimal in Malaysia, Malaysia needs to move faster to allocate the PEVCS at the strategic locations. Regarding this matter, this study aims to determine the suitable criteria for allocating the location of PEVCS in Malaysia. 52 out of 177 sub-criteria and six main criteria items were selected for the Need Analysis in this study as part of the data collecting procedure, which involved 12 respondents. The result revealed that the Need Analysis was used to choose 41 of the sub-criteria, including society (8), economics (10), environment (7), technology (6), accessibility (6), and proximity (4). For future studies, it is recommended to use Likert scales for analysing the data from the Need Analysis, along with calculating the mean and standard deviation values, while utilizing GIS-based MCDM methods to allocate ideal PEVCS locations in Malaysia through the development of a new prediction location model.

Optimisation of a standalone photovoltaic electric vehicle charging station using the loss of power supply probability

The UK is planning to ban the sale of fuel vehicles entirely by 2035 and electric vehicles will be a potential alternative to fuel vehicles. The increase in electric vehicles will increase the charging demand. Standalone charging stations are a potential solution to alleviate the grid challenges of increased charging demand. In this work, the authors investigate a reliability analysis of a 2 MW standalone photovoltaic electric vehicle charging station (PVEVCS) using the loss of power supply probability(LPSP). The PVEVCS model consists of a PV system, a battery energy storage system (BESS) and a CS, using the climate data from Camborne, UK and classifying it into high and low irradiation sections. Next, four different charging demand profiles are selected to examine the models’ LPSP. Later, the chosen charging demand profiles are optimised using various combinations of PV systems, BESS and CS. It is concluded that the different solar irradiation had a significant effect on the LPSP. Under the same combination, higher PV capacity has a more positive impact on reducing daytime LPSP, higher BESS capacity has a more significant effect on lowering nighttime LPSP and larger CS capacity has a more significant impact on declining hourly LPSP.

Analysis and Impacts of Grid Integrated Photo-Voltaic and Electric Vehicle on Power Quality Issues

Electric vehicles (EVs) and photovoltaic (PV) systems have been progressively incorporated into the grid in recent years principally due to two factors: reduced energy costs and lower pollutants. Numerous studies have investigated how integrating PV and EVs into the grid may affect specific people. It is crucial to understand that the electricity grid will experience the combined effects of PV–EV integration as PV and EV penetration increases. The primary motivation for PV’s integration with Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) services is the aim to reduce charging costs from discharging; moreover, another prerequisite must be satisfied before PV arrays will be able to provide V2G services. The range between the driving limit and EV battery degradation should be reasonable. The way EVs charge and discharge will be impacted by these factors. Numerous analyses are required in order to control the power between various source and load scenarios. In order to balance grids and manage frequency, controllers such as Improved Particle Swarm Optimization (IPSO), Improved Ant Colony Optimization (IACO), and Improved Mayfly Optimization (IMO) are used. As a result, V2G/G2V helps feed electricity back into the grid. By providing the proper duty cycle ratio, the proposed controller regulates converter switching. This study allowed for the performance analysis and operation simulation of a grid-connected PV/EV/Grid system. The purpose of this system was to maximize PV self-consumption while maintaining power quality characteristics like harmonics, grid voltage/current, and power factor.

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage.

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions.

Discrete Stochastic Control for Energy Management With Photovoltaic Electric Vehicle Charging Station

This paper develops an intelligent energy management system for optimal operation of grid connected solar powered electric vehicle (EV) charging station at workplace. The optimal operation is achieved by controlling the power flow between the photovoltaic (PV) system, energy storage unit, EV charging station (EVCS) and the grid. The proposed controller is developed considering the PV availability, grid loading and the EV charging load data. This information is modelled using Markov decision process (MDP) to develop a control strategy that eliminates the conventional problem of immediate recharging of energy storage unit after each EV charging by setting a target state of charge (SOC) level. This maximizes the use of PV power for EV charging and minimizes the impact on the grid. To test the operation of the proposed controller, a charging station powered by a 5 kW PV system with 35 kW energy storage unit connected to grid is developed through numerical simulations and experiment. The experiments were carried out for three different conditions under varying irradiance profile and load profile for multiple days. The results estimated the EV load and PV power and optimized the energy storage unit SOC between 0.3-1. Further, the energy management strategy minimized the impact of energy exchange between the grid and charging station by a factor of 2.

Photovoltaic–electric vehicles participating in bidding model of power grid that considers carbon emissions

The synergy of clean energy and electric vehicles (EVs) is highly relevant in achieving low-carbon development. To promote the coordinated development of EVs and photovoltaics (PV) under the background of open power selling, a PV–EV bidding model that considers carbon emissions based on the distributed storage and smart contract technology of a blockchain is proposed in this study. This model aims to provide economic benefits to market players, ensure the safety and environmental protection of power grid operations, and integrate the ladder-type carbon emission trading mechanism. Then, it divides the electricity market into the electricity sales layer, i.e., active distribution networks (ADNs) and PV systems, and the electricity purchase layer, i.e., EVs. Smart contracts are signed with the agent layer, i.e., charging stations. The improved particle swarm optimization algorithm is used to solve the price and power of PV systems and EVs in the optimal contract, and the revenue of PV systems and EVs can be increased by over 35%. Moreover, the orderly charging and discharging of EVs is coordinated with the characteristics of the daily power generation and evening load peaks of PV systems. The proposed model can effectively provide an optimal scheduling scheme for peak load shifting and valley filling while reducing the carbon emissions of ADNs. It also promotes the local consumption of PV.

The Grid Independence of an Electric Vehicle Charging Station with Solar and Storage

The UK government has set a ban on the sale of new petrol and diesel cars and vans by 2030. This will create a shift to electric vehicles. which will present a substantial impact on the grid. Therefore, methods to reduce the charging station’s impact on the grid have to be developed. This paper’s objective is to evaluate how integrating solar and storage affects a charging station’s dependence on the grid. A photovoltaic electric vehicle charging station (PVEVCS) is first designed, and then four charging profiles are selected to assess the station through a simulation using MATLAB. The array produces 3257 MWh/yr which, on average, offsets 40% of the electric vehicle (EV) load experienced by the station. Furthermore, with the integration of storage, the dependence is further reduced by 10% on average. The system also exported energy to the grid, offsetting close to all the energy imported.

Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance

The integration of photovoltaic (PV) systems and electric vehicles (EVs) in the built environment, including at workplaces, has increased significantly in the recent decade and has posed new technical challenges for the power system, such as increased peak loads and component overloading. Several studies show that improved matching between PV generation and EV load through both optimal sizing and operation of PV-EV systems can minimize these challenges. This paper presents an optimal PV-EV sizing framework for workplace solar powered charging stations considering load matching performances. The proposed optimal sizing framework in this study uses a novel score, called self-consumption-sufficiency balance (SCSB), which conveys the balance between self-consumption (SC) and self-sufficiency (SS), based on a similar principle as the F1-score in machine learning. A high SCSB score implies that the system is close to being self-sufficient without exporting or curtailing a large share of local production. The results show that the SCSB performance tends to be higher with a larger combined PV-EV size. In addition to presenting PV-EV optimal sizing at the workplace charging station, this study also assesses a potential SC and SS enhancement with optimal operation through smart charging schemes. The results show that smart charging schemes can significantly improve the load matching performances by up to 42.6 and 40.8 percentage points for SC and SS, respectively. The smart charging scheme will also shift the combined optimal PV-EV sizes. Due to its simplicity and universality, the optimal sizing based on SCSB score proposed in this study can be a benchmark for future studies on optimal sizing of PV-EV system, or distributed generation-load in general.

Enhancing the Power Quality of the Grid Interactive Solar Photovoltaic-Electric Vehicle System

Grid interactive solar photovoltaic (PV) and electric vehicle (EV) systems are the emerging technologies nowadays, mainly due to energy cost reduction and minimization of emission levels. Various research surveys have presented the effect of grid integration of PVs and EVs in an isolated way. However, it is worth accepting that with the continuous emergence of PVs and EVs, the power grid is experiencing the combined effect of PV–EV integration. The distribution system network of EVs impacts the power quality of the grid. In this paper, shunt active power filter is modelled using neuro-fuzzy control technique for the mitigation of harmonics using MATLAB. The improvement in the system performance is analyzed and compared with the traditional compensation techniques.

Combined PV–EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment

Photovoltaic (PV) systems and electric vehicles (EVs) integrated in local distribution systems are considered to be two of the keys to a sustainable future built environment. However, large-scale integration of PV generation and EV charging loads poses technical challenges for the distribution grid. Each grid has a specific hosting capacity limiting the allowable PV and EV share. This paper presents a combined PV–EV grid integration and hosting capacity assessment for a residential LV distribution grid with four different energy management system (EMS) scenarios: (1) without EMS, (2) with EV smart charging only, (3) with PV curtailment only, and (4) with both EV smart charging and PV curtailment. The combined PV–EV hosting capacity is presented using a novel graphical approach so that both PV and EV hosting capacity can be analyzed within the same framework. Results show that the EV smart charging can improve the hosting capacity for EVs significantly and for PV slightly. While the PV curtailment can improve the hosting capacity for PV significantly, it cannot improve the hosting capacity for EVs at all. From the graphical analysis, it can be concluded that there is a slight positive correlation between PV and EV hosting capacity in the case of residential areas. • Combined PV–EV grid integration study in a residential LV grid is presented. • A novel graphical analysis approach for combined PV–EV HC assessment is presented. • Both HC for PV and EV are analyzed in the same framework. • Smart EV charging and PV curtailment are included to assess possible HC enhancement. • Correlation between HC for PV and EV in the case of residential areas is obtained.

Improving Power Quality and Mitigation of Harmonic Distortion Impact at Photovoltaic Electric Vehicle Charging System

This article offers a clear and realistic design for an active power filter to increase reliability and power quality of the photovoltaic charging system and a high-penetration electric vehicle distribution system. The MOPSO algorithm is used as the basis for problems with optimization and filter tuning. A typical regular load curve is used to model the warped power grid over a 24-hour cycle to estimate the total harmonic distortion (THD). For structures with high penetration of electric cars, the probability of minimizing THD (for example to five percent) is explored via optimum capacity active shunt filters and shunt capacitors. To maximize general performance of the charging system, the switching systems are re-scheduled. Moreover, to increase the current control accuracy of shunt active filter, the fuzzy logic controller is utilized. The major drawback to new system is that it would have unrestricted billing for entire day to cope with voltage interruption. In MATLAB / SIMULINK, detailed machine setup and control algorithm experiments are simulated. The simulation findings confirm the efficiency and viability of projected shunt active filter to enhance voltage profile and track power performance of photovoltaic charging system.

Payback Period and Life Cycle Emissions of a Commercial Solar Carport with a Virtual Case Study

The solar carport is a significant technology-oriented infrastructural concept for facilitating electric vehicle charging stations (EVCS). The EVCS predominantly utilise the onsite solar photovoltaic energy for the charging of EVs. Moreover, EVCS can act as multipurpose CS to enable Grid to Vehicle (G2V) and Vehicle to Grid(V2G). Photovoltaic Electric vehicle charging station (PEVCS) can feed both EVs, traditional consumer loads, and can also feed power to the grid. Thus, enabling PEVCs across the various organisations and institutions can meet the local as well as dynamic demands incurred during charging of EVs. In this paper, a detailed economic and system analysis for the PEVCS is carried out using PVSyst and Helioscope for the area planning and shadow analysis. The normalised results of PEVCS is analysed along with the payback period and life cycle emissions are calculated for a virtual case study in Taylor’s University. At the end of the 25th year, based on the analysis, the overall payback and revenue for 25 years is 2,653.6 kMYR will be generated by selling energy at 0.58 MYR / kWh.

Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: a review

Grid integration of solar photovoltaic (PV) systems and electric vehicles (EVs) has been increasing in recent years, mainly with two motivations: reducing energy cost, and reducing emission. Several research studies focuses on the individual impact of grid integration of PVs and EVs. However, it is worth noting that with the increasing penetration of PVs and EVs, the power grid will be experiencing the combined impact of PV–EV integration. To present a thorough understanding, this study first presents a detailed study on the impact of grid integration of PVs and EVs individually, followed by combined impact of PV and EV, on the aspects of grid stability, power quality and energy economics. It has been identified from the literature review that individually PVs and EVs can negatively affect the grid stability and power quality due to the intermittent nature of PV energy and uncertainty of EV load. However, several research works have reported that coordinated operation of the PVs and EVs can negate the issues arising due to individual integration of PVs and EVs. Furthermore, large-scale penetration of PVs and EVs are expected in future energy market, and coordinated operation of them can potentially help lowering energy costs and carbon footprint.

Estimating the spatiotemporal potential of self-consuming photovoltaic energy to charge electric vehicles in rural and urban Nordic areas

The penetration of electric vehicles (EVs) and photovoltaic (PV) systems has increased globally in the last decade. For planning purposes, the spatiotemporal variability of distributed PV power generation and EV charging needs to be quantified for urban and rural areas. This study introduces a state-of-the-art, open, and generally applicable model framework for assessing the spatiotemporal mismatch between EV load and PV generation for urban and rural areas. The model is applied to a rural and an urban area, both 16 km × 16 km and located in Sweden, and is evaluated for the extreme months of January and July. The results show that an energy deficit of, at most, 86% and an up to ten times surplus took place in January and July, respectively. A high self-consumption (SC) of 77% was observed for January and a high self-sufficiency (SS) of 69% for July. This is to say that during July, PV can fulfill 69% of the EV charging load. Moreover, there were no observed correlations between the PV-EV temporal matching scores (the SS and the SC) and the dominant type of charging, e.g., workplace charging in each grid cell (1 km × 1 km) of the areas. This can be partially attributed to the wide distribution of the rooftop orientations in both areas. This challenges the assumption of low PV-EV temporal matching in residential parts of the city. Applying the proposed methodology to other regions is incentivized.

Feasibility Study on Photovoltaic Electric Vehicle Charging Station for Workplace

Feasibility Study on Photovoltaic Electric Vehicle Charging Station for Workplace