Optimizing Energy Efficiency in Electric Vehicles: Intelligent Control System Design

An Electric vehicle (EV) charging station have become a significant in more countries throughout the globe, due to enhance of utilizing EV against conventional vehicles. Moreover, initiation of charging stations, and an architecture effect of household photovoltaic power development reduce the feed-in tariff. An Energy Management Strategy (EMS) and its real-time adjustment capability control a much on a fuel economy of a hybrid EV (HEV). This research proposes an energy-efficiency strategy based on optimization of driving patterns for an EV. The EMS algorithm called Artificial Neural Network (ANN) based Hybrid Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) is proposed. The proposed ANN-PSOGWO model makes the benefits of ultracapacitor when restricting a battery distribution current and produces the switching transition between Ultra-Capacitor (UC) charging mode and discharging mode. The simulation results display that proposed ANN-PSOGWO achieved the better results on the operation cost such as initial SOC of 0.4, Final SOC of 0.298, time of 145.3s and distance of 2258m when compared to the existing methods when compared to the PSO and GWO methods.