Search

Abstract Resonant inductive‐based wireless power transfer (WPT) for battery charging has potential applications in electric vehicles (EVs). The EV battery charging process requires the regulation of both charging voltage and current. Duty ratio or frequency control is generally preferred to manage the power flow between the transmitter and receiver coils in the WPT system. In the case of WPT charging, misalignment between the coils and parameter variations are unavoidable issues that result in changes to the output power. Therefore, it is essential to control the power flow to maintain constant current (CC) and constant voltage (CV) modes during battery charging. To address these challenges, various primary‐side control techniques, such as asymmetric clamped mode (ACM), asymmetric duty cycle (ADC), and phase‐shift (PS) fixed frequency control strategies, have been proposed for WPT systems. This paper conducts a comparative analysis of these control methods, considering their output voltage ranges and their ability to maintain zero‐voltage switching (ZVS) for the entire control range. Furthermore, the paper presents a generalized design for reduced‐order small signal modelling, utilizing an extended describing function. The designed controller, based on small signal modelling, will undergo real‐time testing to evaluate its dynamic performance in the series‐series resonant converter.

Solar Photovoltaic (SPV) panel manufacturers provide a datasheet, which contains key electrical specifications of the SPV module for the user's reference. However, these data alone prove inadequate in anticipating the performance of PV systems under fluctuating atmospheric conditions. The approach put forth in this paper ascertains five parameters of a PV module using information supplied by the manufacturer. This approach considers the impact of solar radiation and cell temperature. The paper outlines the process for determining the parameters of the five-parameter model and establishes a comparison between projected current-voltage curves and manufacturer-provided data. To extract the unknown parameters of a single diode model Adaptive Particle Swarm Optimization (APSO) techniques with barrier constraints was developed. The study investigates both multi-crystalline and mono-crystalline PV module technologies to validate the efficacy of the proposed method. Results demonstrate that the proposed technique, besides being straightforward, proves adept at accurately simulating the dependable performances of PV modules. Comparative analysis reveals that the proposed methods extract parameters with minimal modeling errors and high precision, regardless of temperature fluctuations, outperforming conventional PSO methods.

The transportation sector is one among the key sources of greenhouse gas emissions (GHGs) leading to climate change and global warming. Energy transition through electrified transportation is one of the solutions to tackle the issues. Electric vehicles (EVs) offer significant environmental and economic advantages against the conventional Internal Combustion Engine (ICE) vehicles. EVs are called mobility loads and their connectivity to the utility grid for charging is unpredictable. The large penetration of such unpredictable loads into the utility grid will lead to undesirable impacts on the utility service. This paper highlights the importance of managing and optimizing the charging schedules. The optimization of EV charging has diverse aspects, and the perspectives of EV charging differ among consumers, aggregators, and utility services. Proper planning and management of EV charging is essential to achieve harmony amongst these stakeholders. A comprehensive review on the objectives of electric vehicle charging optimization from various perspectives is presented and discussed in this paper. EV charging optimization techniques including mathematical programming, meta heuristics algorithms and machine learning techniques are explored. The main objectives, constraints, strength, and limitations of different charging optimization techniques are analyzed in detail. A brief discussion on the communication strategies for data exchange in EV charging framework is presented and the need for a communication security constrained EV charging scheduling is also emphasized.

In this article, a hybrid Maximum Power Point Tracking (MPPT) approach that can quickly adapt to a fast changing weather conditions is proposed. The proposed method entails the combination of a Perturb and Observe (P&O) MPPT technique with a Look-Up Table (LUT) algorithm. The algorithm operates through a series of stages to attain the MPP with improved speed and accuracy, in response to the observed variations in the input(s). The efficacy of the proposed approach’s MPP tracking performance is assessed through comprehensive simulation studies and experimental testing across diverse input conditions. The comparative evaluation of the proposed two-stage algorithm involves an assessment of its tracking response, reliability, and accuracy in relation to other established methodologies. The algorithm proposed exhibits a satisfactory level of overall efficiency, as evidenced by a tracking efficiency of 99.31% even under frequently changing weather conditions. This confirms the validity of the technique employed.

This paper discusses FPGA-based power sharing control of a solar PV-powered low-voltage DC (LVDC) nanogrid. The proposed LVDC nanogrid control algorithm monitors power generation, storage, and load power and shifts between sources and load to maintain power balance. The controller effectiveness is studied for varying input and varying load conditions via simulation studies and hardware implementation. A proposed system simulation model with a control algorithm is built into MATLAB and performance analysis is carried out. The power sharing control algorithm and MPPT algorithm are built using Xilinx blocks in MATLAB and programmed into FPGA controller Furthermore, experimentation is carried out on the proposed model to validate the simulation results. Hardware results obtained under different atmospheric and load conditions are presented and discussed.

This paper explores the intricate challenges that are impeding the widespread adoption of Electric Vehicles (EVs) and explores the concerted efforts of the research community towards addressing these obstacles. The surge in interest surrounding EVs as a sustainable transportation alternative is undeniable, yet several hurdles persist in hindering their mass acceptance. From limitations in battery technology and charging infrastructure to concerns over range anxiety and manufacturing sustainability, these challenges form a multifaceted barrier. However, the research community has been actively engaged in tackling each issue with innovative solutions. Advancements in battery chemistry and energy storage, coupled with improvements in charging networks and smart grid integration, are poised to reshape the EV landscape. Moreover, studies on user behavior, public policy, and lifecycle analysis are contributing to the development of holistic strategies for enhancing EV adoption. By delving into these challenges and the ongoing research endeavors, this paper sheds light on the evolving pathway towards a future where EVs can thrive as a mainstream mode of transportation. Also, an analysis is conducted to evaluate the economic viability of EVs based on daily range considerations, with the objective of determining which category of users would benefit most from adopting EVs. Furthermore, policies are proposed that are aimed at establishing a harmonious and balanced EV ecosystem.

A maximum power point tracking (MPPT) technique plays an important role to ensure maximum photovoltaic (PV) output power is extracted under stochastic weather conditions. The research to date tends to focus on developing a standalone optimization MPPT algorithm rather than looking into a hybrid MPPT algorithm. This paper introduces particle swarm optimization (PSO) to optimize the maximum PV output power and to determine the best design variable for penalizing the step size of the conventional methods namely the perturb and observe (PO) and the incremental conductance (IC). With the help of the hybrid MPPT algorithm (PSO+IC and PSO+PO), the step size is no longer fixed, and it is changing according to the solar irradiance. To evaluate the proposed hybrid algorithm, a single-stage grid connected PV system is designed for several different scenarios with various weather conditions. The performance of the hybrid MPPT algorithm and the conventional methods is compared. The results demonstrate that the hybrid MPPT algorithm is remarkably better than the conventional methods, especially for PSO+IC, where it only takes 43.4 ms of tracking time and reaches the efficiency of 99.07% under standard test conditions.

In this paper, a two-input, single-output (TISO) DC-DC converter for electric vehicle charging applications with solar photovoltaic (PV) as one of the sources is discussed. A novel, simple, and effective control strategy with maximum power point tracking (MPPT) to maximize power from PV while maintaining a constant bus voltage is proposed. A 100 V, 100 W TISO DC-DC converter is designed and comprehensive simulations are conducted under different conditions. The system characteristics are validated by comparing the results with a conventional P&O algorithm and multiple step size P&O algorithm. The proposed control strategy can precisely generate control signals to track MPPT while maintaining the bus voltage by controlling the output of a fixed DC source according to the changes in PV generation. The overall efficiency of the proposed approach is 98.67%, and the average output voltage is 99.09 V under selected conditions. The overall average output voltage ripple is 0.22% with the proposed approach, while it is 2.91% and 1.55% with the conventional P&O and multiple step size P&O MPPT techniques, respectively. Further, a 500 V, 17 kW high-power converter is designed and a simulation is carried out. The high-power converter provides an average output voltage of 496.92 V with an overall efficiency of 98.72%, and the average output voltage ripple is 0.16%. The results obtained from the simulation under the selected conditions lead to the conclusion that the converter has better efficiency and less variation in the output voltage at higher power levels with the proposed control technique.