Routing Approaches used for Electrical Vehicles Navigation: A Survey

Abstract

The growing demand for Electronic Vehicles (EVs) depends on the high integration of this technology in many areas. Thus, an important area of research raises interest to find the optimal path-planning solution for electric vehicles. This paper discusses several reviews and analyzes some of the constraints of the techniques used to improve these systems. The paper discusses common models used in Unmanned Ariel Vehicles (UAV) and Unmanned Ground Vehicles (UGV). This paper investigates the planning approaches that lead to finding the optimal route for the tour from the source to the destination. The review outlines the different models and systems of Unmanned Ariel Vehicles (UAV) and Unmanned Ground Vehicles (UGV). This paper can be considered as comprehensive survey research for EV routing techniques to assist researchers choose the appropriate approach for developing a system based on optimization techniques, machine learning, or Hybrid Approaches (HA) techniques. Optimization techniques are mostly used to find the optimal path and achieve multi-objective goals. Some findings were approved as the best models inspired by natural biological as genetic algorithms, Particle swarm optimization, and Ant colony optimization. In addition to machine learning techniques as Reinforcement Learning. The hybrid approach techniques that combine optimization and machine learning techniques can increase robustness in solving routing problems