Efficient path planning and battery management for electric vehicles

Abstract

The rapid advancement in battery technology has brought electric vehicles (EVs) into reality, and the increasing adoption of autonomous electric vehicles (AEVs) has presented significant challenges. Existing research in the realm of IoT has extensively explored EV transportation systems, focusing on aspects like routing, energy management, and grid system equilibrium. In this context, this thesis readdresses the challenge of determining the fastest route for AEVs considering the battery charging time. Diverging from the current state-of-the-art, our work delves into the prospect of not only minimizing travel time but also maximizing battery life for the optimal utilization of electric vehicles. We commence by formalizing the problem of ”Efficient Path Planning and Battery Management for Electric Vehicles” as a mixed integer linear programming (MILP) model, thereby deriving its optimal solutions mathematically. Given the inherent complexity of the optimization model, we introduce a range of heuristic algorithms designed to address the problem at scale. Furthermore, this problem is similar to the traveling salesman problem(TSL), which means it has an NP-hard nature. [...]