Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Abstract

With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs.

Quality of Service and Associated Communication Infrastructure for Electric Vehicles

“…The developed communication framework was analyzed and compared with four different scenarios: (i) a pull-mode communication framework; (ii) accessing real-time information from the buses; (iii) direct communication between EVs and CSs; (iv) a centralized case communication framework. Practical experiments were conducted on two public transport routes, and the results were verified [110,111]. Adopting the proposed communication framework may allow future improvements to the quality of service (QoS) in EVs, particularly in terms of the queue length and resource allocation.…”

Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance

“…In [19], based on graph reinforcement learning, environmental information is extracted to learn fast charging guidance strategies, reduce EV charging time, and balance the operation status of charging stations. Combining real-time data and utilizing the A* algorithm to solve the path for EVs, as described in [20]. In [21], a path optimization scheme is proposed that combines both topographic and traffic information to reduce energy consumption during EV travel.…”