# An Efficient GPS Algorithm for Maximizing Electric Vehicle Range
## 2 Literature Review  
Numerous studies have been conducted in this field to determine the most efficient route, considering variables such as distance, traffic, road elevation, and vehicle energy consumption [10]. While route planning has traditionally relied on algorithms like Dijkstra [11], adapting them to suit the unique requirements of electric vehicles has proven challenging. The authors of [12] introduced a multi-objective optimization technique aimed at balancing energy usage and travel time in EV route planning. This approach seeks to optimize both time and energy efficiency simultaneously by identifying Pareto-optimal solutions. [PERSON] and [PERSON] conducted a comprehensive review of the green vehicle routing problem, highlighting challenges such as limited battery range, charging infrastructure, and energy consumption. They also surveyed various optimization techniques employed in the literature, including exact algorithms, metaheuristics, and hybrid methods [13; 14]. [PERSON] et al. proposed an energy consumption optimization model for the electric vehicle routing problem with time windows. Their model considers both energy consumption and time windows simultaneously, emphasizing the importance of optimizing both objectives. They also provided a thorough review of optimization techniques utilized in the existing literature [14; 15]. Also, as mentioned by [PERSON] et al., advances in routing optimization with a hybrid BSO method tailored for 4 PL challenges enhance search efficiency and solution accuracy by mimicking beetle foraging behavior. This technique is crucial for electric vehicle (EV) routing, addressing logistical issues, and improving operational efficiency [16]. Similarly, the same authors have explored a unique delivery strategy combining drones and riders, optimizing delivery with a framework that considers time frames, capacity limits, and energy usage, aligning with EV range maximization and sustainable practices [17]. Additionally, another study introduces an ACO method for energy-efficient EV path planning, factoring in road incline, traffic, and charging station placement, emphasizing adaptive algorithms to minimize energy use and improve EV routing accuracy [18]. [PERSON] et al. [19] propose a comprehensive technique to EV charging scheduling. It addresses the use of time-of-use (TOU) pricing to optimize charging periods and reduce peak demand on the power system. The paper proposes methods which achieve a compromise between economic efficiency and grid stability using efficient algorithms. This research is relevant to our study because it emphasizes the need for optimizing numerous parameters that influence EV energy use, such as charging timings and patterns. Integrating these insights into route planning algorithms has the potential to improve the success of EV range maximizing tactics, particularly when external elements like aerodynamics and wind conditions are considered [19]. Similarly, [PERSON] et al. investigate the complexity of scheduling EVs among several depots while keeping in mind power grid restrictions. This study examines the logistical obstacles of EV adoption, highlighting the importance of power grid features in the design process. The study's focus on optimizing routes and timetables to guarantee effective energy consumption is consistent with our goal of increasing EV range. By combining multi-depot scheduling insights and grid interaction dynamics, our MATLAB Simulink model may be further modified to account for real-world restrictions, thereby boosting the accuracy and reliability of energy consumption estimates verified using real-life data [20]. Therefore, this study aims to address these challenges by examining the energy consumption of electric vehicles across different road topologies and evaluating the impact of various vehicle models, wind speed, and direction on energy consumption. The aim is to identify the most environmentally friendly route for specific vehicles by mathematically analyzing various factors and employing a MATLAB Simulink model.