| dc.description.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. [...] | en_US |
