Efficient Path Planning Framework Considering Dynamic Behavior and Energy Constraint of Electric Vehicle

In recent times, electric vehicles (EVs) are garnering attention not merely as environmentally sustainable modes of transportation, but also in the domains of competitive racing and autonomous driving technologies. The driving range of EVs, which are fitted with batteries and motors, is dictated by the total energy capacity of the electric battery, in contrast to vehicles powered by internal combustion engines. Nevertheless, research rarely focuses on the development of efficient path planning methodologies that generate a driving trajectory incorporating both physical location and speed for EVs, taking into account their unique characteristics. This contrasts with the extensive research dedicated to path planning for internal combustion engine vehicles. Thus, we propose a framework for path planning that has efficient travel time while considering the specific characteristics of EVs and their energy limitations. We first build a data-based energy consumption model of the target vehicle by collecting data from real driving on the track. Next, we enhance the traditional optimal path-planning algorithm RRT* to account for vehicle limitations and to use a cost function that consists of driving time. We also suggest heuristic algorithms to meet the given total energy capacity limitation in the vehicle by reducing the vehicle’s acceleration as part of the framework. The framework shows that when the vehicle follows an efficient driving path, increasing driving time by 3.75 % decreases total energy consumption by 8.1 % as a trade-off. These results indicate that an efficient driving path was derived by considering both vehicle characteristics and energy consumption in relation to speed.