Iterative Learning-Based Decentralized Model Predictive Charging Control for Plug-In Electric Vehicles

Plug-in electric vehicles (PEVs) charging process significantly impacts the efficiency and even the safety of the electrical power grid. In this work, we study a decentralized framework of PEVs charging problem with a coordination task. An iterative learning-based model predictive charging control algorithm is developed for achieving the valley-filling performance. The design of the decentralized model predictive control meets the individual charging requirements. The iterative learning method approximates the electricity price function and the system state sampled safe set to improve the accuracy of optimization problem calculations. The decentralized problem, in which individual PEV minimizes its own charging cost, is formulated based on the sum of all power loads. Simulation results demonstrate the performance and verify the effectiveness of the proposed framework.