A deep reinforced markov action learning based hybridized energy management strategy for electric vehicle application

Abstract

Due to the severe lack of fossil fuels and existing environmental pollution, developing Electric Vehicles (EV) powered by clean, new energy is essential to finding a way to address this problem. The hybrid EV powered by fuel cell is the most practical and suitable option in present days. Several researchers have concluded that a key factor in determining an EV's performance is the hybrid energy management system's output performance. Moreover, proper energy management and performance optimization are the most essential solutions to ensure the normal operation of the hybridized system. In the existing studies, several energy management methodologies are developed using a hybridized energy sources, yet it facing some challenges in terms of high cost, increased system designing complexity, stability issues, and unreliable power supply. Therefore, the proposed work intends to implement a novel Energy Management System (EMS) for EVs with the use of advanced learning algorithms. In the proposed topology, the combination of Proton Exchange Membrane Fuel Cell (PEMFC), battery, and Super-Capacitor (SC) have been used as the major energy sources. Then, a high gain DC-DC converter is utilized to boost the regulate output voltage with reduced stress and loss factors. For improving the performance of converter, a recently developed Gorilla Troop Optimization Algorithm (GTOA) is implemented, which provides the best solution to choose the controlling parameters for generating the switching pulses. Moreover, the Deep Reinforced Markov Action Learning (DRLA) is employed to assure an efficient energy management in EV systems. For validation, the simulation and comparative analyses are carried out in this study with the use of numerous parameters. Index termsElectric Vehicle (EV), Proton Exchange Membrane Fuel Cell (PEMFC), Battery, Super-Capacitor (SC), Renewable Energy Sources (RES), High Gain DC-DC Converter, Gorilla Troop Optimization Algorithm (GTOA), and Deep Reinforced Markov Action Learning (DRLA).