OPTIMIZING

To understand the popularity of electric vehicles circa 1900, it is also important to understand the development of the personal vehicle and the other options available. At the turn of the 20th century, the horse was still the primary mode of transportation. Steam emerged as a reliable energy source with a proven track record, notably powering factories and locomotives. In the late 1700s, steam also played a role in some of the earliest self-propelled vehicles. However, despite its early adoption in various applications, it wasn't until the 1870s that steam technology began to gain traction in the automotive industry. One significant reason for the delayed adoption of steam technology in cars was its impracticality for personal vehicles. Steam-powered vehicles faced several challenges that hindered their widespread use. For instance, they required considerable startup times, often up to 45 minutes, particularly in cold conditions. Additionally, steam vehicles needed frequent refilling with water, which imposed limitations on their range and practicality for everyday use. These drawbacks underscored the challenges associated with steam-powered cars and contributed to their eventual decline in favor of alternative propulsion methods, such as internal combustion engines and electric motors, which offered greater convenience and efficiency for personal transportation. As electric vehicles came onto the market, so did a new type of vehicle, the gasoline-powered car thanks to improvements to the internal combustion engine in the 1800s. Although gasolinepowered vehicles had potential, they were not without problems. They took a lot of human labor to operate because shifting gears was a difficult operation, and starting them required turning a hand crank, which some drivers found challenging. Gasoline-powered vehicles were also notorious for their noisy engines and nasty exhaust. (TOTAL ENERGIES, 2020) In contrast, electric cars did not suffer from the issues associated with steam or gasoline vehicles. They were quiet, easy to drive, and did not emit the noxious pollutants characteristic of other cars of the time. Consequently, electric cars rapidly gained popularity among urban residents, particularly women. They proved ideal for short journeys within the city, especially considering the poor road conditions outside urban areas, which limited the travel range of all types of vehicles. (Nilesh Wani, 2020)

This project investigates improving pathfinding algorithms in Geographic Information Systems (GIS) by optimizing the calculation of geodesics. Geodesics refer to the shortest paths along the curved surface of the Earth, as opposed to straight lines drawn on a flat map. This is crucial for accurate navigation, especially over long distances. Traditional GIS pathfinding algorithms often rely on simpler Euclidean distance calculations, which can lead to significant errors.The objective of this study is to develop or improve upon existing methods for finding optimal geodesics paths within a GIS environment. This will enable more accurate and efficient navigation for various applications, such as: route planning for vehicles, pedestrians, and drones, search and rescue operations, ecological studies analyzing animal movement patterns. The study will explore different algorithms for calculating geodesics on a geoid (Earth's mathematical representation). This could involve techniques like Dijkstra's algorithm adapted for curved surfaces or A* search with appropriate heuristics for geodesic distances. The study might explore methods to optimize the pathfinding process. This could involve strategies like pre-computing geodesics for frequently used routes or implementing techniques to reduce computational complexity. This study by optimizing geodesics paths for navigation has the potential to significantly enhance the capabilities of GIS for various applications requiring accurate and efficientpathfinding.

One of the most challenging problems associated with the use of solar energy in low-income households is the high initial cost of installation the bulk of which is the cost of the battery energy storage system. The project was implemented using the ATMEGA 382P microcontroller due to its superior code efficiency, enabling throughputs up to ten times faster than conventional microcontrollers. The prototype, designed and simulated on Proteus, incorporated three loads connected within
the system. Power was supplied by a DC Buck converter module, ensuring stable and efficient energy provision. This combination of advanced microcontroller technology, multiple load integration, and an efficient power source lays the groundwork for a robust and highperforming system. Thorough incorporation of a rule-based algorithm using time of day and battery capacity as
criteria in addition to proper classification of loads, the proposed system reduced the domestic energy usage and improved system availability. To verify the efficiency and robustness of the proposed algorithm, a test lab was set up, and the obtained results were compared in terms of total energy consumption, cost, the improved battery storage duration