Smart and efficient EV charging navigation scheme in vehicular edge computing networks

• Published in: Journal of cloud computing : advances, systems and applications Vol. 12; no. 1; pp. 176 - 15
• Main Authors: Li, Haoyu, Chen, Jihuang, Yang, Chao, Chen, Xin, Chang, Le, Liu, Jiabei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Deep reinforcement learning; Driving conditions; Edge computing; Electric vehicle charging; EV charging navigation; Information Systems Applications (incl.Internet); Integer programming; Intelligent transportation systems; Machine learning; Mixed integer; Navigation; Roads & highways; Roadsides; Route planning; Route selection; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Traffic; Traffic congestion; Transportation networks; Travel time; User experience; Vehicular edge computing networks; EV charging navigation; Deep reinforcement learning; Route planning; Vehicular edge computing networks
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-023-00547-y

With the increasing number of electric fast charging stations (FCSs) deployed along roadsides of both urban roads and highways, the long-distance travel of electric vehicles (EVs) becomes possible. The EV charging navigation scheme is significant for the quality of user experience. However, the variable conditions of both power grid and traffic networks make it a serious challenge. In this paper, we propose an efficient EV charging navigation scheme while considering both the electric and computation resource sharing. With the support of vehicular edge computing networks in intelligent transportation systems (ITSs), EVs perform both the flexible power load and edge computing nodes. When the traffic network in the established route starts to become congested, EVs can select to enter the nearest FCS. In addition to being supplemented by electric resources, EVs also benefit by sharing their own computing resource with FCSs. We formulate the EV charging navigation as a mixed integer programming problem, the EV moving route planning, FCS selection, and staying time in FCSs are optimized, to balance the relationships among the traveling time, traveling cost and reward. To address the influence caused by the randomness of traffic conditions and charging prices, a two-stage charging navigation algorithm combined with A ∗ algorithm and deep reinforcement learning (DRL) is proposed, with a novel designed reward function. Eventually, numerous experimental results show the effectiveness of the proposed schemes.