scholarly journals PV and Grid interfaced Plug-in EV Battery Charger operating in P-VG, P-V and V-G Modes

2019 ◽  
Vol 8 (2) ◽  
pp. 3431-3443 ◽  
Keyword(s):  
Control Strategies ◽  
Dynamic Control ◽  
Simulation Software ◽  
Solar Irradiation ◽  
Irradiation Condition ◽  
Battery Charging ◽  
Charging System ◽  
Pv Panel ◽  
Irradiation Level ◽  
Utility Grid

The proposed system facilitates uninterruptable charging of a photovoltaic (PV) fed plug-in electric vehicle (EV) battery charging system irrespective of solar irradiation conditions by integrating utility grid to the battery charging system. The system employs bidirectional cycloconverter (BDCC) in order to use utility grid as source or sink during different modes of operation which depends on the availability of solar power. During low irradiation condition, the utility grid acts as a backup source in order to facilitate uninterruptable charging of the EV battery. When surplus power is generated from the PV panel, it is fed to the utility grid, which acts as sink in this mode. For uninterruptable EV battery charging, the controller operates the switches and relays in the proposed system corresponding to solar irradiation level. The available literatures define complex control strategies which are solved in this proposed system by adopting a simple dynamic control algorithm. The simulation of the proposed system has been carried out using PSIM simulation software and experimental prototype has been designed, developed and tested for different modes of operations to validate the efficacy of the proposed system.

scholarly journals Performance study of MATLAB modelled PV panel and conventional PV panel interfaced with LabVIEW

2018 ◽  
Vol 7 (2.17) ◽  
pp. 70
Author(s):  
Jaiganesh K ◽  
Karuppiah N ◽  
Ravivarman S ◽  
Md Asif
Keyword(s):  
Electrical Energy ◽  
Black Body ◽  
Atmospheric Temperature ◽  
Climatic Conditions ◽  
Solar Irradiation ◽  
Irradiation Condition ◽  
Solar Pv ◽  
Performance Study ◽  
Pv Panel ◽  
Proposed Model

The maximum electrical energy conversion efficiency of the Solar PV panel is up to 22% in normal conventional roof- top system under the temperature of 25˚C on Standard Test Condition (STC). In Indian climatic conditions, the atmospheric temperature is mostly above 35˚C to 45˚C, it incites 35˚C to 80˚C temperature on the PV panel. The black body of the PV panel absorbs more heat. This temperature affects the electrical efficiency of the panel significantly. This paper proposes the mathematical modelling of the solar PV panel for different solar irradiation and the temperature. The experimental evaluation is conducted in the latitude of 11.36 (N) and longitude 77.82 (E). The testing and monitoring was done with LabVIEW based National Instruments hardware such as NI cDAQ-9178, NI DAQ - 9227 and NI DAQ 9225. The comparative study between the simulated result and real time hardware results are discussed in this paper. The test result shows that the output of the proposed model mismatches with the experimental output of the solar PV panel due to the negative correlation between the efficiency and temperature for variable irradiation condition. It shows a power difference of 9.41W between the output of the proposed model and the experimental setup.  

Hybrid battery charging system using solar PV and utility grid

2014 ◽  
Author(s):  
Dhiwaakar Purusothaman S R R ◽  
Ramesh Rajesh ◽  
Karan K Bajaj ◽  
Vineeth Vijayaraghavan ◽  
Venkatesan M
Keyword(s):  
Solar Pv ◽  
Battery Charging ◽  
Charging System ◽  
Utility Grid

scholarly journals Design of Adaptive Neuro-Fuzzy Inference Control Based One-Axis Solar Tracker on Battery Charging System

2020 ◽  
Vol 190 ◽  
pp. 00015
Author(s):  
Imam Abadi ◽  
Tiara Oktavia Hardiana ◽  
Chairul Imron ◽  
Dwi Nur Fitriyanah ◽  
Yahya Jani ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Maximum Temperature ◽  
Voltage Response ◽  
Peak Time ◽  
Battery Charging ◽  
Inference System ◽  
Charging System ◽  
Pv Panel ◽  
Neuro Fuzzy ◽  
Solar Tracker

The photovoltaic (PV) panel can produce electrical energy that is very environmentally friendly and easy to use. The use of PV panels is suitable for supplying peak loads or at night using batteries as energy storage. However, the battery needs to manage for control, and the battery can last long. The solution to battery management problems is through research about the battery charging system. The DC-DC converter used is the Single Ended Primary Inductance Converter (SEPIC) type. Voltage Control of the battery charging using Adaptive Neuro-Fuzzy Inference System (ANFIS). In the simulation of bright conditions, ANFIS controls can track the charging point set point and obtain a voltage response with a rise time of 0.0028 s, a maximum overshoot of 0.027 %, a peak time of 0.008 s, and a settling time of 0.0193 s. When charging a solar tracker, PV battery gets a 0.25 % increase compared to a fixed PV panel. PV solar tracker can follow the direction of the sun’s position. The irradiation value and maximum temperature affect the input voltage and input current that enters the converter.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

Comparison between Seven MPPT Techniques Implemented in a Buck Converter

2019 ◽  
Vol 12 (6) ◽  
pp. 476-486
Author(s):  
Lahcen El Mentaly ◽  
Abdellah Amghar ◽  
Hassan Sahsah
Keyword(s):  
Value Added ◽  
Buck Converter ◽  
Maximum Power ◽  
Solar Irradiation ◽  
Solar Panels ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Pv Panel ◽  
Low Efficiency ◽  
Power Point

Background: The solar field on our planet is inexhaustible, which favors the use of photovoltaic electricity which generates no nuisance: no greenhouse gases, no waste. Methods: It is a high value-added energy that is produced directly at the place of consumption through photovoltaic (PV) solar panels. Notwithstanding these advantages, the maximum power depends strongly on solar irradiation and temperature, which means that a Maximum Power Point Tracking (MPPT) controller must be inserted between the PV panel and the load in order to follow the Maximum Power Point (MPP) continuously and in real time. In this work, MPP’s behavior was simulated at different temperatures and solar irradiations using seven techniques which identify the MPP by different methods. Results: The novelty of this work is that the seven MPPT methods were compared according to a very selective criterion which is the MPPT efficiency as well as a purely digital duty cycle control without using the PI controller. The simulation under the PSIM software shows that the FLC, TP, FSCC, TG, HC and IC methods have almost the same efficiency of 99%, whereas the FOCV method had a low efficiency of 96%. Conclusion: This makes it possible to conclude that the best methods are FLC, HC and IC because they use fewer sensors compared to the rest.

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