Contract-Based Charging Protocol for Electric Vehicles With Vehicular Fog Computing: An Integrated Charging and Computing Perspective

Electric vehicles (EVs), one of the most effective solutions to reduce gas emission and realize fossil fuels replacement, are enjoying growing popularity from governments to customers. The development of EVs leads to significant advances in vehicle automation and electrification, but meanwhile poses additional heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing (VFC) and smart EV charging for joint optimization and propose an integrated charging and computing (IC2) architecture for EV-included smart grid. In the proposed IC2 architecture, charging stations are profit-driven third-party power prosumers that also help compute tasks offloaded by smart grid while EVs act as both energy consumers and computation providers. We employ the contract theory to provide a multiattribute contract-based charging protocol for EVs and charging stations in an information asymmetry scenario. To obtain the optimal contract, we derive KKT conditions and design a convex-concave-procedure-based contract optimization algorithm. We also design a heuristic offloading algorithm to assign heterogeneous tasks toward different EVs. Numerical results indicate that the proposed multiattribute contract-based charging-computing scheme can effectively benefit both the charging stations and EVs, and meanwhile improves the task computation capability in EV-integrated smart grid.

Automated summary

This paper proposes an integrated charging and computing (IC2) architecture, which combines vehicular fog computing (VFC) and smart EV charging for optimization of electric vehicle (EV) charging and computing tasks in a smart grid. A multiattribute contract-based charging protocol is provided using contract theory, and a convex-concave-procedure-based contract optimization algorithm is designed. A heuristic offloading algorithm is also proposed for task assignment to heterogeneous EVs. Numerical results show that the proposed multiattribute contract-based charging-computing scheme effectively benefits both the charging stations and EVs, while improving task computation capability in the EV-integrated smart grid.