Electric Vehicle Charging System 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.

Application of safety and stability optimization algorithms for charging connection devices in high-power charging systems

In response to the safety and stability issues of current electric vehicle charging connection devices, this study proposes a charging system planning for electric vehicles with different capacity charging piles based on the user behavior characteristics of electric vehicles and Monte Carlo methods. It is found that the predicted results under the set management strategy are most consistent with the trend of actual load changes. Moreover, in the prediction of weekly load, the research strategy has better performance than traditional unmanaged strategies. Under the research scheme, the average charging speed of charging piles with capacity of A and B in the peak period was 41.4 min/ and 18.8 min/, respectively, which increased by 29.3% and 11.7% respectively compared with 58.6 min/ and 21.3 min/ in the normal period. The total economic cost of the research plan was 4.871 million yuan, which was 67.0 million yuan and 3.833 million yuan lower than the control methods 1 and 2, respectively. The total number of charging stations of types a and b that need to be purchased for the research method decreased by 18.47% and 63.24% compared to the comparative method 3. The results indicate that the research method significantly improves the utilization rate of charging stations in the electric vehicle charging system. This study has important application value in the intelligent management of electric vehicle charging systems.

Renewable sources-based efficient electric vehicle charging system through power forecasting and charge scheduling

Renewable sources-based efficient electric vehicle charging system through power forecasting and charge scheduling

Grid-Tied Solar Integrated Electric Vehicle Charging System with Advanced LMS Control

Grid-Tied Solar Integrated Electric Vehicle Charging System with Advanced LMS Control

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).

Enhancing Electric Vehicle Charging Systems With a Versatile Bidirectional Buck Boost DC to DC Converter Integrated With Solar Photovoltaic Technology

Enhancing Electric Vehicle Charging Systems With a Versatile Bidirectional Buck Boost <scp>DC</scp> to <scp>DC</scp> Converter Integrated With Solar Photovoltaic Technology

A Comparative Analysis of Static and Dynamic Wireless Charging Systems for Electric Vehicles

The movement to electric vehicles (EVs) is central to sustainable transportation and necessitates improvements in effective and easy charging devices. This article compares static and dynamic wireless charging systems for electric vehicles, focusing on their operation, feasibility, and practical applications. Static charging involves inductive power transfer when the vehicle is parked, while dynamic charging allows power transfer while the vehicle is in motion. The paper explores efficiency, infrastructure needs, cost implications, and user convenience, highlighting potential benefits and challenges. The findings aim to inform stakeholders in the automotive and energy sectors about optimal wireless charging strategies for enhancing electric vehicle adoption.

Improving Area-Controlled Error and Frequency Stability for Electric Vehicle Charging System

Improving Area-Controlled Error and Frequency Stability for Electric Vehicle Charging System

Modeling and prediction of conducted EMI in on-board charging system of electric vehicle

Modeling and prediction of conducted EMI in on-board charging system of electric vehicle

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.

An Integrated Electric Vehicle Charging System of Wireless Power Transfer and Auxiliary Power Module With Shared Converter and Magnetic Coupler

An Integrated Electric Vehicle Charging System of Wireless Power Transfer and Auxiliary Power Module With Shared Converter and Magnetic Coupler

A new GaN-based converter design for electric vehicle charging system

A new GaN-based converter design for electric vehicle charging system

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.

A hybrid YDSE - THDCNN approach based multi objective optimization of energy management for renewable energy sources with electric vehicles

Thermal power plants have long been essential to the fossil fuel-based electricity supply chain, according to statistics studies. Nevertheless, due to the enormous initial and ongoing expenses of modern power plants as well as their negative environmental effects. This manuscript proposes a hybrid technique for grid-connected load-following hybrid photovoltaic and wind microgrid with a grid-connected electric vehicle charging system. The proposed hybrid technique is the joint execution of both Young's double-slit experiment optimizer (YDSE) and the Tree Hierarchical Deep Convolutional Neural Network (THDCNN). Hence, it is named as YDSE-THDCNN. The major objective of the proposed technique is to reduce the fuel, operation, and maintenance costs, as well as to reduce the environmental costs and maximize the efficiency of the system. The proposed YDSE technique is utilized to generate the control signal of the inverter and the optimal signal will be predicted by the THDCNN. By then, the MATLAB working platform has adopted the proposed way, and the existing method is used to compute its execution. The proposed method outperforms any existing techniques, including the Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Particle Swarm Optimization (PSO) Algorithm. The existing method shows the cost of 2.6$, 3.8$, 4.6$, and the proposed method shows the cost of 1.3$ which is lower than the other existing method.

Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.

Optimizing Room Temperature Control: A Comparative Analysis of Deep Reinforcement Learning and Contextual Bayesian Optimization Methods

Building temperature control presents significant modeling challenges due to the complexity of internal systems and varied usage patterns. Consequently, black box modeling has become a prevalent approach in this domain. This technique broadly encompasses deep learning and Bayesian optimization methods, each offering unique mechanisms for deriving optimal control parameters. Deep learning methods iteratively refine control parameters using extensive datasets, whereas Bayesian optimization employs Gaussian process regression and acquisition functions to pinpoint optimal solutions. This paper conducts a comparative analysis of two innovative methods within these branches: the application of Deep Reinforcement Learning (DRL) for integrated control of household temperature and electric vehicle charging, and the Primal-Dual Contextual Bayesian Optimization (PDCBO) method for adaptive thermal control in buildings. The study reveals that the DRL approach, integrated with electric vehicle charging systems, excels in responsiveness and reducing energy consumption. In contrast, the PDCBO method enhances control by allowing for the artificial definition and continuous optimization of constraints on energy usage and comfort during the operation, facilitated by algorithmic design. By synthesizing the strengths of both methods and considering electric vehicle integration and tailored adaptive control constraints, enhanced outcomes are achievable. This paper proposes that specific control strategies be developed for different building types to address diverse real-world requirements effectively.

Stability control and analysis of hydrogen production using a multi-terminal DC EV charging system with PV

In the shift towards sustainable energy, DC microgrids have emerged as efficient and reliable alternatives to traditional AC systems, particularly suited for low-voltage applications. The adoption of electric vehicles (EVs) in multi-terminal DC (MTDC) systems introduces challenges in maintaining voltage stability, particularly during master station failures. Our research introduces a decentralized control strategy with multi-mode switching to ensure stability under such conditions. We conduct a comprehensive stability analysis of MTDC systems heavily integrated with EVs, identifying the instability risks and proposing mitigation strategies. The effectiveness of our control approach is validated through simulation studies and a detailed state-space model. Additionally, we explore the integration of renewable energy sources and hydrogen production into MTDC systems for EV charging, which minimizes the need for a new power infrastructure and enhances renewable energy use. This integration optimally reduces renewable energy discard rates, thereby improving energy efficiency without compromising system performance. Our study provides practical insights and theoretical support for renewable energy-driven hydrogen production within DC microgrids. The proposed scheme's performance is validated through simulation studies using a state-space model.

ANFIS-based control of resonant converters for optimized charging system of electric vehicle (EV) batteries

This paper presents a resonant converter-based Electric Vehicle (EV) battery charging module utilizing Proportional-Integral (PI) and Adaptive Neuro-Fuzzy Inference System (ANFIS) control for an optimized charging system. The EV charging module is integrated with a resonant converter comprising a full bridge, HFTF, and DBR. The module utilizes a Resonant Converter which reduces the switching loss incurred during converter operation at high frequency by offering ZCS or ZVS at the switching time. A standard PI Controller manages the duty ratios of the primary full bridge switches with tuned gains. The CC and CV controllers each have their own PI Controller for current and voltage, respectively. To enhance the performance of the EV System, the standard PI Controllers in both the CC and CV control systems are replaced with ANFIS Controllers which are trained as per the data generated by the CC and CV control using an optimization technique that controls the duty ratio of the switches. The proposed ANFIS-based and PI-based control strategy provides an adaptive and flexible approach to control the battery voltage and current by intelligent adjustment of Constant Current (CC) and Constant Voltage (CV) operation modes and the passive elements switching across specific ranges of State-Of-Charge (SOC) to enhance the performance and safety of the charging system. MATLAB Simulation results demonstrated that the proposed ANFIS-based control reduces current ripple content compared to PI-based control. The ANFIS Controller improves overall battery performance, reliability, and stability, which makes it a better choice for next-generation EV charging systems.

Foreign object detection technology in wireless charging system for electric vehicles

Foreign object detection technology in wireless charging system for electric vehicles

A hybrid carrier‐based discontinuous PWM with neutral‐point voltage ripple and current distortion reduction for Vienna rectifier

AbstractThe three‐level Vienna rectifier is widely used in industrial applications, such as electric vehicle charging systems and telecommunication power systems. Discontinuous pulse width modulation (DPWM) is widely used in three‐level AC/DC converters due to its features of switching loss minimization. However, the problems of neutral‐point voltage ripple, current zero‐crossing distortion, and switching losses are mutually coupled for the Vienna rectifier. To address these issues, a hybrid carrier‐based discontinuous pulse width modulation (HCB‐DPWM) with reduced neutral‐point (NP) voltage ripple and current distortion is proposed. First, the /6 clamping period of the conventional DPWM is divided into three‐type clamping intervals, which reduce NP voltage ripple with shortened clamping period. Then, the elimination of current distortion around the current zero‐crossing point and the switching loss reduction are investigated. The implementation of the proposed HCB‐DPWM is given in detail. Finally, the simulation and experimental results of the Vienna rectifier are presented to validate the performance that the proposed HCB‐DPWM can eliminate current zero‐crossing distortion and reduce neutral‐point voltage ripple with different modulation indices.