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Battery, Fuel Cell, and Super Capacitor are energy storage solutions implemented in electric vehicles, which possess different advantages and disadvantages.
In EVs, the type of energy storage is, together with the drive itself, one of the crucial components of the system.
Another alternative energy storage for vehicles are hydrogen FCs, although, hydrogen has a lower energy density compared to batteries.
This article proposes an integrated approach that combines stationary and vehicle-mounted mobile energy storage to optimize power system safety and stability under the conditions of limiting the total investment in both types of energy storages.
The switched-capacitor-based inverter design that is being suggested produces five-level output voltages with only two capacitors, one DC source, and seven switching devices. A low-frequency half-height approach is utilized to generate the firing pulses of switching devices in a standalone system for higher output voltage quality and lower THDs.
The flying capacitor inverter combines low semiconductor costs and gives a multi-level output with high output frequency and low dynamic losses. Although the input is only two level with no need for the enormous DC-link capacitor bank, the output is multi-level and the output frequency is a multiple of the switching frequency.
As the converter and inverter blocks have separate controls, this capacitor serves as the voltage reference for the inverter. Implementing photovoltaic (PV) systems as direct power sources for motors without batteries is a complex process that requires a sophisticated control mechanism.
The simulations based on 3kW grid connected PV system are carried out in DIgSILENT Power Factory software. Findings: A capacitor of 410µF is needed to be connected in parallel with a 3kVA inverter having an nominal input voltage of 370V and maintaining a voltage ripple under 8.5%.
Literature associated with the DC fast chargers is categorized based on DC fast charging station design, optimal sizing of the charging station, CS location optimization using charging/driver behaviour, EV charging time at the station, and cost of charging with DC power impact on a fast-charging station.
A fast-charging station should produce more than 100 kW to charge a 36-kWh electric vehicle's battery in 20 min. A charging station that can charge 10 EVs simultaneously places an additional demand of 1000 kW on the power grid, increasing the grid's energy loss [ 68 ].
However, it is noteworthy that existing research on fast charging station planning predominantly focuses on losses and voltage stability, often overlooking these critical V2G studies. The datasets used and generated during the current study are available from the corresponding author upon reasonable request.
The paper underscores the imperative for fast charging infrastructure as the demand for EVs escalates rapidly, highlighting its pivotal role in facilitating the widespread adoption of EVs. The review acknowledges and addresses the challenges associated with planning for such infrastructure.
This study proposes a method for detecting and localizing solar panel damage using thermal images. The proposed method employs image processing techniques to detect and localize hotspots on the surface of a solar panel, which can indicate damage or defects.
Yet, several operational and environmental conditions can damage solar panels and lower their performance. To maintain effective operation and maintenance of solar power facilities, prompt diagnosis and localization of solar panel damage are essential. A popular non-destructive testing method for spotting damage to solar panels is thermal imaging.
This person is not on ResearchGate, or hasn't claimed this research yet. This research paper explores the use of deep learning, specifically the YOLOv11 model, in detecting defects in solar panels using thermal imaging. The focus is on two common types of faults: Hotspot Faults and Bypass Diode Faults.
The solar modules got fired at California and North Carolina which are showed as the examples of the faults. The EL images are taken for the healthy panels and the spots of the minor cracks, break images, and finger impregnations for fault-finding. Then, by the PCA and ICA for the image to be processed by the component analysis.
