Prediction-based charging of PHEVs from the smart grid with dynamic pricing

Coexistence of Plug-in Hybrid Vehicles (PHEVs) with the emerging smart grids has been recently an attractive and equally challenging research topic. The existing electricity grids are rapidly evolving into smart grids by utilizing the advances in Information and Communication Technologies (ICT). Meanwhile, advances in Lithium-Ion (Li-ion) battery technologies have made manufacturing of PHEVs cost-wise effective, and PHEVs are expected to be widely adopted in the following years. PHEVs have several benefits over conventional vehicles such as, less fuel dependency, lower operating costs and lower amount of CO₂ emissions. On the other hand, unless PHEVs are powered by off the grid renewable energy resources, they will be drawing electricity from the grid to charge their batteries and they will increase the load on the grid. In the worst case, when the Time Of Charging (TOC) coincides with the critical peak periods, the grid may experience overall or partial failure. For most of the cases, TOC may be during the peak hours when the price of electricity is high. To avoid endangering grid resilience and to avoid high costs, a charging strategy and communication with the smart grid is essential. In this paper, we propose a prediction-based charging scheme which receives dynamic pricing information by wireless communications, predicts the market prices during the charging period and determines an appropriate TOC with low cost. Our prediction-based charging scheme is based-on a simple, light-weight classification technique which is suitable for implementation on a vehicle or a charging station. We show that prediction-based charging provides less operating cost and less CO₂ emissions.