  In this paper, we propose a novel energy-efficient framework for an electric
vehicle (EV) network using a contract theoretic-based economic model to
maximize the profits of charging stations (CSs) and improve the social welfare
of the network. Specifically, we first introduce CS-based and CS
clustering-based decentralized federated energy learning (DFEL) approaches
which enable the CSs to train their own energy transactions locally to predict
energy demands. In this way, each CS can exchange its learned model with other
CSs to improve prediction accuracy without revealing actual datasets and reduce
communication overhead among the CSs. Based on the energy demand prediction, we
then design a multi-principal one-agent (MPOA) contract-based method. In
particular, we formulate the CSs' utility maximization as a non-collaborative
energy contract problem in which each CS maximizes its utility under common
constraints from the smart grid provider (SGP) and other CSs' contracts. Then,
we prove the existence of an equilibrium contract solution for all the CSs and
develop an iterative algorithm at the SGP to find the equilibrium. Through
simulation results using the dataset of CSs' transactions in Dundee city, the
United Kingdom between 2017 and 2018, we demonstrate that our proposed method
can achieve the energy demand prediction accuracy improvement up to 24.63% and
lessen communication overhead by 96.3% compared with other machine learning
algorithms. Furthermore, our proposed method can outperform non-contract-based
economic models by 35% and 36% in terms of the CSs' utilities and social
welfare of the network, respectively.
