Energy Management System using Cascaded Neural Network for a Hybrid Electric Vehicle

A Hybrid Electric Vehicle (HEV) and an evolution of Electric Vehicles (EV) have achieved engrossment in day to day life because of its advantage like structural flexibility, improvement towards air quality, noise free and comfort. The performance of Energy Management System (EMS) is hampered by complex configuration and behaviour of multi-source hybrid energy systems. The goal of EMS is to instantly regulate energy flows from converters to meet control objectives, regardless of architecture of powertrain. In this paper, a novel bidirectional isolated DC-DC converter together with cascaded Artificial Neural Network (ANN) controller is used to enhance system input voltage from the Diode Bridge Rectifier (DBR) with medium power applications. Furthermore, the adoption of cascaded ANN controller supports in regulating current and voltage individually. A MATLAB simulation followed by hardware analysis is performed to determine the steady state operation of converter. According to the simulation results, EMS with vehicle system designation greatly increase fuel economy.