A robust MPPT framework based on GWO-ANFIS controller for grid-tied EV charging stations DOI Creative Commons
Debabrata Mazumdar, Pabitra Kumar Biswas, Chiranjit Sain

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Abstract As electric vehicles gain popularity, there has been a lot of interest in supporting their continued development with the aim enhancing dependability, environmental advantages, and charging efficiency. The scheduling navigation for is among most well-known research topics. For optimal scheduling, coupled network state between transportation power networks must be met; moreover, outcomes might significantly impact these networks. To address climate challenges, relying only on fossil fuel-based infrastructure car insufficient. Consequently, Multi-Energy Integrated EV stations have emerged as workable solution that seamlessly integrates grid power, renewable energy sources—particularly solar energy—and needs. enhanced grey wolf optimised (GWO) ANFIS controller Maximum Power Point Tracking (MPPT), standby battery systems, neural network-integrated grids, sophisticated control algorithms like PID are all proposed this article energy-efficient terminals vehicles. Moreover, authors had considered four conditional case study help MATLAB/Simulink 2018a software, design thoroughly examined assessed, providing viable route an efficient sustainable infrastructure.

Language: Английский

Improved BRBFNN-based MPPT algorithm for coupled inductor KSK converter for sustainable PV system applications DOI

K. S. Kavin,

P. Subha Karuvelam,

M. Murali

et al.

Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Language: Английский

Citations

2
An Enhanced MPPT Approach Based on CUSA for Grid-Integrated Hybrid Electric Vehicle Charging Station DOI Creative Commons
Debabrata Mazumdar, Pabitra Kumar Biswas, Chiranjit Sain

et al.

International Journal of Energy Research, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 14

Published: April 3, 2024

Because of the fluctuating demands for electricity and growing awareness need to protect environment from global warming depletion nonrenewable natural resources, battery-powered electric vehicles, or EVs, are being used in transportation sector as an alternative internal combustion engine vehicles. However, charging these EVs with conventional fossil fuels is neither economically sustainable nor structurally viable. Therefore, this manuscript proposes a renewable energy-powered EV station featuring combination solar energy, standby battery systems, sophisticated control techniques such neural network-integrated grids, enhanced Cuckoo Search Algorithm Maximum Power Point Tracking, Proportional-Integral-Derivative controller. This idea beats current methods presents viable way drive revolution while lessening environmental effects. It maximizes energy management guarantees steady power supply even erratic weather. Grid integration ensures consistency supplies at terminals. When compared other algorithms that have been investigated literature, designed algorithm exhibits excellent performance. integration, addition battery, essential ensuring has constant supply, during unpredictable

Language: Английский

Citations

9
Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives DOI
Maher G. M. Abdolrasol, Afida Ayob, Molla Shahadat Hossain Lipu

et al.

eTransportation, Journal Year: 2024, Volume and Issue: 22, P. 100374 - 100374

Published: Oct. 30, 2024

Language: Английский

Citations

7
Optimized ANFIS-based robust nonlinear control of a solar off-grid charging station for electric vehicles DOI Creative Commons

Bibi Tabassam Gul,

Iftikhar Ahmad, Habibur Rehman

et al.

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 20361 - 20373

Published: Jan. 1, 2025

Language: Английский

Citations

0
A High‐Performance MPPT Solution for Solar DC Microgrids: Leveraging the Hippopotamus Algorithm for Greater Efficiency and Stability DOI Creative Commons
Debabrata Mazumdar, Taha Selim Ustun, Chiranjit Sain

et al.

Energy Science & Engineering, Journal Year: 2025, Volume and Issue: 13(5), P. 2530 - 2545

Published: April 17, 2025

ABSTRACT The rapid growth of modern civilization has led to increased global warming and climate challenges. Variations in atmospheric temperature, sunlight intensity other factors significantly impact the performance photovoltaic (PV) systems. To maximize energy production, these systems must operate efficiently at their Maximum Power Point under varying weather conditions. This study introduces a new Hippopotamus Algorithm (HA) designed for Tracking (MPPT) solar PV connected direct current (DC) microgrids. Performance HA's is compared with three established optimization algorithms: Grey Wolf Optimization, Cuckoo Search Particle‐Swarm Optimization across different operating scenarios partial shading circumstances. Obtained results demonstrate that HA not only achieves higher power output but also responds faster than existing methods. In each conditions, efficiency range proposed methods are 82.16% 89.92%, respectively, temperature variation case 84.67% which far better approaches. As per stability concerns, HA‐based MPPT approach attains minimal settling time gives steady‐state stable its load both shading, fluctuation A comparative analysis shown tabular form this article. Additionally, it effectively manages bidirectional flow fluctuating ensures resilient sustainable architecture low generating situations when DC microgrid integrated an system.

Language: Английский

Citations

0
Developing a resilient framework for electric vehicle charging stations harnessing solar energy, standby batteries and grid integration with advanced control mechanisms DOI Creative Commons
Debabrata Mazumdar, Pabitra Kumar Biswas⃰, Chiranjit Sain

et al.

Energy Science & Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Abstract A direct consequence of the rapid expansion civilization and modernization trends is escalation in global warming consequential climatic upheavals. The world has actively advocated adoption electric vehicles (EVs) as a response to environmental challenges posed by vehicular emissions. It evident that conventional fuel‐based charging infrastructures are economically impractical lack organizational cohesion light proliferation EVs. An EV station powered renewable energy presents promising opportunity for enhancing flexibility control. imperative stations be equipped with solar power standby batteries (SBBs). Consequently, this article evaluates system utilizes proportional‐integral‐derivative controller, neural network‐equipped grid utilizing Dragon Fly Optimization Algorithm generate maximum point tracking controller. To achieve optimal management within station, MATLAB/Simulink used implement rigorously test proposed system. orchestrates interaction between panel, backup battery, Compared existing systems literature, comprehensive exhibits commendable efficiency. Due pivotal role played integration SBB, can ensure reliable supply under any weather conditions.

Language: Английский

Citations

3
Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data DOI Creative Commons
Bappa Roy, Shuma Adhikari, Subir Datta

et al.

Electricity, Journal Year: 2024, Volume and Issue: 5(4), P. 843 - 860

Published: Nov. 4, 2024

Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional can struggle with rapid fluctuations in irradiance and temperature. This paper introduces a novel deep learning-based algorithm that leverages Long Short-Term Memory (LSTM) neural network (DNN) effectively track maximum power from PV panels, utilizing real-world data. The simulations three algorithms—Perturb Observe (P&O), Artificial Neural Network (ANN), proposed LSTM-based MPPT—were conducted using MATLAB (2021b) RT_LAB (24.3.3) an OPAL-RT simulator real-time analysis. data used this study were sourced NASA/POWER’s Native Resolution Daily Data irradiation temperature specific Imphal, Manipur, India. obtained results demonstrate system achieves superior tracking accuracy under changing conditions, producing average output 74 W. In comparison, ANN P&O yield outputs 57 W 62 W, respectively. significant improvement, i.e., 20–30%, underscores effectiveness LSTM technique enhancing By incorporating data, valuable insights into generation selected location are provided. Furthermore, model verified through OP4510, showcasing applicability approach scenarios.

Language: Английский

Citations

3
Design of a triple port integrated topology for grid-integrated EV charging stations for three-way power flow DOI Creative Commons
Harshita Tiwari, Arnab Ghosh, Subrata Banerjee

et al.

Frontiers in Energy Research, Journal Year: 2024, Volume and Issue: 12

Published: Nov. 8, 2024

Environmental fluctuations, solar irradiance, and ambient temperature significantly affect photovoltaic (PV) system output. PV systems should be efficient at the Maximum Power Point in various weather climates to maximize their potential power The Tracking (MPPT) technique is employed plan a specific location that yields maximum amount of power. Operating dispersed alternative energy sources connected grid this situation makes control an unavoidable task. This research article suggests designing electronics converter topology links sustainable resources electric vehicles grid. There are four modes operation for proposed topology: grid-to-vehicle, vehicle-to-grid, renewable-to-vehicle, renewable-to-grid discussed. three electronic converters uses discussed, controllers also designed maintain balance stability all cases. battery characteristics indicate operating mode. work primarily focuses on converter’s Triple Port Integrated Topology (TPIT) flow voltage control. Here, integrate TPIT with systems-the grid, renewable energy, vehicles-into one system. source array cells integrated using unidirectional bidirectional DC-DC converters. future scope investigate adding additional ports integrating other resources, such as hydrogen fuel or sources, create more versatile robust management EV charging stations.

Language: Английский

Citations

1
Performance Analysis of Modulation Strategies in Single-Phase HBNPC Inverter Across Variable Operating Conditions DOI Creative Commons
Nazlıcan Çavli, E. Ozkop

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 65997 - 66013

Published: Jan. 1, 2024

This study investigates the performance of a single-phase 5-level H-Bridge Neutral Point Clamped (HBNPC) inverter across various operating conditions. These conditions encompass variations in output power, frequency, modulation index, and techniques. To adapt strategies typically employed 3-phase structures, we apply them to HBNPC inverter. The under consideration include level-shifted (LS) based on phase disposition pulse width (PD-PWM), third harmonic injection (THI-PWM), space vector (SV-PWM), modified PWM (MPWM). are developed using different types reference signals (RS) carrier (CS). Initially, analyze inverter's voltage current waveforms strategy states. Subsequently, explore relationships between efficiency, total distortion (THD), variable power We uncover distinct patterns strategy-THD, for maximum power. Furthermore, examine relationship index THD each state, considering switching frequencies. Additionally, investigate frequency values, approaching analysis from multiple perspectives.

Language: Английский

Citations

0
A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna DOI Open Access
Nupur Chhaule, Chaitali Koley, Sudip Mandal

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(19), P. 3819 - 3819

Published: Sept. 27, 2024

A significant advancement in wireless communication has occurred over the past couple of decades. Nowadays, people rely more on services offered by Internet Things, cloud computing, and big data analytics-based applications. Higher rates, faster transmission/reception times, coverage, higher throughputs are all necessary for these emerging 5G technology supports features. Antennas, one most crucial components modern gadgets, must be manufactured specifically to meet market’s growing demand fast intelligent goods. This study reviews various antenna types detail, categorizing them into two categories: conventional design approaches machine learning-assisted optimization approaches, followed a comparative antennas reported publications. Machine learning (ML) is receiving lot emphasis because its ability identify optimal outcomes several areas, it expected key component our future technology. ML demonstrating an evident predicting behavior expediting with accuracy efficiency. The analysis performance metrics used evaluate another focus assessment. Open research problems also investigated, allowing researchers fill up current gaps.

Language: Английский

Citations

0