Electric Vehicle Charging Research Articles

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.

A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement

This paper presents a novel and practical data-driven approach to sub-optimally allocate charging stations for electric vehicles (EVs) in an early-stage setting. Specifically, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed in order to promote the transition of EVs from traditional cars? We develop a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -nearest model and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -nearest model that can capture people's satisfaction towards a certain design and formulate the early-stage EV charging station placement problem as a monotone submodular maximization problem utilizing fine-grained population, trip, transportation network and POI data. A greedy-based algorithm is proposed to solve the problem efficiently with a provable approximation ratio. A case study of Haikou is provided to demonstrate the effectiveness of our approach.

Optimizing Sustainable Urban Mobility: Integration Of Electric Vehicle Charging Infrastructure Into Smart Buildings

The integration of electric vehicle (EV) charging infrastructure into smart buildings is a crucial step toward enhancing sustainable urban mobility. This paper explores the benefits, challenges, strategies, and best practices associated with this integration, with a focus on optimizing EV charging accessibility and promoting a cleaner transportation ecosystem. Traditional vehicles powered by fossil fuels contribute significantly to pollution and greenhouse gas emissions, highlighting the urgent need for sustainable alternatives. EVs offer reduced emissions, lower operating costs, and potential energy independence through renewable sources, making them a promising solution for urban mobility challenges. Despite these advantages, the widespread adoption of EVs faces hurdles, particularly regarding charging infrastructure availability and accessibility in urban areas. Range anxiety among EV users due to insufficient charging stations is a significant concern hindering the transition to electric transportation. The paper emphasizes strategic placement of charging stations, integration with renewable energy sources like solar panels, and utilization of smart grid technologies to optimize EV charging operations. Case studies such as Tesla Gigafactory Shanghai and Googleplex showcase successful implementations, demonstrating scalability, user-centric design, and seamless integration with building management systems. Key challenges such as funding constraints, technical interoperability, and grid integration are addressed, proposing solutions like innovative financing models, standardization efforts, and advanced grid management technologies. By addressing these challenges and implementing sustainable solutions, the integration of EV charging infrastructure into smart buildings can significantly contribute to reducing environmental impact, promoting cleaner transportation options, and fostering a more sustainable and resilient urban environment

Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

A new method for thermal runaway warning of electric vehicle charging

Abstract With the large-scale development of electric vehicle (EV) in China, the frequent occurrence of EV fire accidents has attracted the attention of insiders to the fault monitoring and early warning during the charging process of EV. According to data from the National Emergency Management Ministry, there were over 3,000 EV fire accidents nationwide in 2023, underscoring the urgency for enhanced fault monitoring and early warning measures. In response to these issues, this article proposes an EV thermal runaway early warning method based on Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) by combining the two technologies. The method aims to improve EV safety by monitoring battery status for early thermal runaway warning. First, the historical normal charging data of the battery are preprocessed and feature extracted using the TCN, and then features fed into GRU for time-series modeling and forecasting. This combined model not only demonstrates high prediction accuracy and stability but also swiftly responds to abnormal conditions during charging, effectively preventing thermal runaway accidents and ensuring charging safety. Furthermore, this model possesses excellent generalization capabilities and can adapt to different types and specifications of EV battery systems.

Flexibility potential quantification of electric vehicle charging clusters

A significant obstacle to providing flexibility services with electric vehicles (EVs) is the uncertainty surrounding the profitability and flexibility potential of charging clusters when utilised as a flexible load. Currently, there is a lack of comprehensive and easily applicable methods for quantifying flexibility in the literature. This paper introduces an evaluation tool and a set of flexibility indexes to assess the capability of charging clusters to deliver flexibility services. The method is designed to evaluate and quantify the flexibility potential of charging clusters in terms of short-term and long-term power adjustments and charge scheduling. Through sensitivity analysis, we examine how connection capacity, EV battery capacities, power capabilities, and the number of daily charging sessions influence the flexibility potential of charging clusters. Our findings highlight a direct relationship between the grid connection capacity of clusters and their ability to perform short-term power adjustments. Moreover, while larger batteries tend to reduce energy and time flexibility, their increased storage capability facilitates managing and scheduling a larger energy volume. Furthermore, for the days analysed, the flexibility potential showed minimal sensitivity to the number of daily charging sessions. Instead, the amount of energy requested and connection patterns emerge as key determinants of overall flexibility. In summary, this research provides valuable insights that can inform the design, monitoring, and assessment of EV charging clusters when evaluating their suitability for various flexibility services.

A Physics-Informed and Attention-Based Graph Learning Approach for Regional Electric Vehicle Charging Demand Prediction

A Physics-Informed and Attention-Based Graph Learning Approach for Regional Electric Vehicle Charging Demand Prediction

A Coupled Game Theory and Lyapunov Optimization Approach to Electric Vehicle Charging at Fast Charging Stations

A Coupled Game Theory and Lyapunov Optimization Approach to Electric Vehicle Charging at Fast Charging Stations

Developing an optimal building strategy for electric vehicle charging stations: automaker role

Developing an optimal building strategy for electric vehicle charging stations: automaker role

Comparative Experimental Study on Monofacial and Bifacial Photovoltaic Noise Barriers

Solar energy utilization in the transportation sector is important for reducing fossil fuel consumption and achieving the grant goal of carbon neutrality. The photovoltaic noise barriers (PVNB) are recognized as a potential alternative for electric vehicle charging. This study aims to evaluate the power generation capabilities of both monofacial photovoltaic noise barriers (mono‐PVNB) and bifacial photovoltaic noise barriers (bi‐PVNB) when applied along roads with different directions and shading conditions. Results show that the daily yields of bi‐PVNB facing west, southwest, south, and southeast are 754, 819, 1101, and 894 Wh, respectively. In contrast, the daily yields of mono‐PVNB are 459, 711, 968, and 764 Wh. The bifacial gains are, respectively, 64, 15, 14, and 17%. The influence of partial shading on PV power generation is tested, and sharp decrease is observed when horizontal shading reaches 20% and vertical shading reaches 40%. In summary, the bi‐PVNB shows satisfactory power generation ability with different orientation and shading conditions. Under Shenzhen climate, the annual power generation of bi‐PVNB along east–west, north–south, southeast–northwest, and southwest–northeast direction roads are predicted to be 304, 325, 342, and 335 MWh per kilometer.

Future of Gasoline Stations

The gasoline fueling stations’ future during the automobile industry shift towards electric is questionable. With more than 196,000 gasoline fueling stations in the United States, many of them require an upgrade to electric charging stations. The regular fueling time from gasoline stations is significantly less than the charging time from an electric charging station. Preparedness for shifting from gasoline stations to electric vehicle (EV) charging stations requires detailed research on how consumer behavior changes given they will have a readily available charging port in their households. It would be essential to categorize the customers based on their preferences and thus plan the approach for meeting their needs. For large gasoline stations, the shift to EV charging stations becomes a viable choice as they tend to attract trucks and other travelers throughout the year. A model of electrical infrastructure was prepared for a large gasoline station by using the estimated number of customers visited. Improvement of amenities or devising a way to attract customers will be required to allow them to spend the waiting duration comfortably.

Optimal solution for PV integrated with smart grid connected fast charging EV station: A case study for Jaipur

Current research study investigates the optimal solution for integrating photovoltaic (PV) solar panels with a smart grid-connected fast-charging electric vehicle (EV) station in Jaipur, India, using HOMER software for simulation. The study aims to develop a cost-effective and sustainable charging infrastructure specifically designed for e-rickshaws and personal vehicles, targeting reduced electricity costs. The findings demonstrate that a hybrid Solar and Grid-integrated EV charging station significantly lowers operational costs while promoting renewable energy adoption. The optimized Solar + grid system, compared to a traditional grid-only model, achieves substantial savings, with an average annual energy bill reduction of ₹26,17,806. Despite an initial capital expenditure of ₹50,22,895, the system's financial viability is affirmed by a rapid payback period of 1.9 years (simple) or 2.1 years (discounted) and an impressive internal rate of return (IRR) of 51.3%. Over a 25-year project lifespan, the system is projected to generate total savings of ₹6,54,45,150. The analysis considers various financial metrics, including monthly and annual energy costs, demand charges, and fixed charges, providing a comprehensive overview of the economic benefits. The results underline the importance of incorporating solar power into EV charging stations to reduce overall energy expenses and enhance long-term sustainability. This study offers valuable insights for policymakers, energy managers, and stakeholders, highlighting the economic and environmental advantages of solar-integrated charging solutions in urban settings.

Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).

Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

User Behavior in Fast Charging of Electric Vehicles: An Analysis of Parameters and Clustering

User Behavior in Fast Charging of Electric Vehicles: An Analysis of Parameters and Clustering

Modeling Future Solar and Wind Energy Source Applications for Power Generation at Public Electric Vehicle Charging Stations in Airport Parking Areas Using HOMER-Grid

Modeling Future Solar and Wind Energy Source Applications for Power Generation at Public Electric Vehicle Charging Stations in Airport Parking Areas Using HOMER-Grid

Optimizing Electric Vehicle Charging Infrastructure: A Site Selection Strategy for Ludhiana, India

Optimizing Electric Vehicle Charging Infrastructure: A Site Selection Strategy for Ludhiana, India

Investigating the potential advancements for electric vehicle charging technologies through various converters topology

Investigating the potential advancements for electric vehicle charging technologies through various converters topology

Power system benefits of simultaneous domestic transport and heating demand flexibility in Great Britain’s energy transition

The increasing share of variable renewables in power generation leads to a shortage of affordable and carbon neutral options for grid balancing. This research assesses the potential of demand flexibility in Great Britain to fill this gap using a novel linear optimisation model PyPSA-FES, designed to simulate optimistic and pessimistic transition pathways in National Grid ESO Future Energy Scenarios. PyPSA-FES models the future power system in Great Britain at high spatiotemporal resolution and integrates demand flexibility from both smart charging electric vehicles and thermal storage-coupled heat pumps. The model then optimises the trade-off between reinforcing the grid to align charging and heating profiles with renewable generation versus expanding dispatchable generation capacity. The results show that from 2030, under optimistic transition assumptions, domestic demand flexibility can enable an additional 20–30 TWh of renewable generation annually and reduce dispatchable generation and distribution network capacity by approximately 20 GW each, resulting in a total cost reduction of around £5bn yearly. However, our experiments suggest that half of the total system cost reduction is already achieved by only 25% of electric vehicles alone. Further, the findings indicate that once smart electric vehicle charging reaches this 25% penetration rate in households, minimal benefits are observed for implementing smart 12-hour thermal storages for heating flexibility at the national level. Additionally, smart heating benefits decrease by 90% across all metrics when only pre-heating (without thermal storages) is considered. Spatially, demand flexibility is often considered to alleviate the need for north–south transmission grid expansion. While neither confirmed nor opposed here, the results show a more nuanced dynamic where generation capacities are moved closer to demand centres, enhancing connectivity within UK sub-regions through around 1000 GWkm of additional transmission capacity.

Multi-objective optimal sizing and techno-economic analysis of on- and off-grid hybrid renewable energy systems for EV charging stations

Integrating electric vehicle charging stations (EVCSs) with renewable energy systems requires the consideration of several factors during the planning stage, including environmental impact, economic viability, grid reliability, and self-sufficiency. Therefore, this study conducts a multi-objective optimal sizing of on- and off-grid hybrid renewable energy systems for EVCSs. The sizing problem is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II). Subsequently, the best suitable solutions from the obtained non-dominated solutions are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, prioritizing the objective functions based on diverse interests of different stakeholders (large and small private investors and governmental entities). Finally, a techno-economic analysis is made considering payback period, profitability index (PI), and internal rate of return (IRR). The results show that on-grid systems show high economic viability with payback periods between 1.98 and 7.72 years, an average PI of 5.07 and an average IRR of 23.97%. Although off-grid systems present lower economic viability with payback periods between 8.77 and 22.42 years, an average PI of 1.68 and an average IRR of 4.91%, in certain cases they reach investable levels with payback periods below 10 years, PI above 2, and IRR above the interest rate.