Joint Path Selection, Energy Trading, and Task Offloading in Electric Vehicle Charging and Computing Network

• Published in: IEEE internet of things journal Vol. 11; no. 10; pp. 17067 - 17081
• Main Authors: Rong, Shichu, Zhong, Weifeng, Huang, Xumin, Kang, Jiawen, Xie, Shengli, Yuen, Chau
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Computation offloading; Computational modeling; Costs; Edge computing; electric vehicle (EV); Electric vehicle charging; Electric vehicles; energy trading; Heuristic; Internet of Things; Mixed integer; Networks; Optimization; path selection; Servers; Task analysis; Travel time
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3357861

With the advancement in battery technology and the rise of onboard computing capabilities, electric vehicles (EVs) can serve as both energy prosumers and computing nodes. The mobility of EVs allows them to perform wide-area multiresource exchange in both electricity networks and edge computing networks. We call such a paradigm an EV charging and computing network (EVCCN). It is considered that the EVCCN is composed of multiple charging and computing stations (CCSs) in different locations. Each CCS integrates EV chargers and an edge server, offering the interfaces for EVs to bidirectionally trade both energy and computing resources. We propose a customized model jointly optimizing the path selection, charging/discharging, and task offloading in different CCSs to minimize an EV's travel cost (i.e., the money spent on the EV's trip). In the proposed model, the EV consumes energy and generates data on its way to the destination, subject to travel time, energy, and data constraints. The cost minimization problem is formulated as a nonconvex mixed-integer problem from a user-centric perspective. To solve it fast in practice, we construct a new action-expanded network to simply the model and develop a heuristic based on piecewise McCormick to quickly obtain a near-optimal solution. Simulation results show that our heuristic is computationally efficient for large traffic networks compared with global solvers. We also present results in a traffic network based on Guangzhou city, which shows that our model can save 33.99% in the travel cost compared with a baseline model.