Stochastic firefly algorithm enabled fast charging of solar hybrid electric vehicles

Abstract

Urbanization and population growth has led to an increase in the per unit energy consumption. As majority of the energy is derived from fossil fuel based sources, they have created adverse effects on the environment. Efforts to safeguard the environment have led to the development of hybrid electric vehicles. Proliferation of EV in the markets depends on factors like price, battery technology, economy and development of fast charging stations. Fast charging stations are an important factor in EV market penetration. This paper focusses in designing fast charging solar hybrid EV charging station. To decrease dependence on the grid power and to increase profitability, the charging station includes a 100 kW solar power plant with storage. The paper presents a comprehensive model of the hybrid solar charging station. To determine the load on the station, a stochastic model is used to predict the arrival time, the SOC and the charging demand. Stochastic firefly algorithm (SFA) is used for MPPT control to obtain maximum power from the solar power plant and to ensure fast charging of the station batteries. The station is designed to supply power from the batteries during off-peak hours and from the grid during high peak hours. The paper also presents a multi-objective planning using SFA to minimize the investment cost and to increase the profit of the charging station. Results show that using SFA enables fast charging of the batteries and also increases the profit of the charging station. Higher profit will encourage utilities to invest in charging station and hence this will help in higher penetration of EVs in the market.