Modeling and simulation of an evolutionary approach based on MAS strategy: for intelligent energy management in EV

Electric vehicles and hybrid vehicles have become one of the relevant and sustainable solutions for the transportation of tomorrow. They are the most presenting and studying by the research; seen their advantages in terms of composition and movement. Within this type of vehicles, energy management is a fundamental tool to ensure smooth operation during the rolling cycle, while optimizing available energy sources and even predicting future power demand. This paper proposes a method of optimization and prediction of energy based on the Markov chains. The work requires the development of a suitable system to control the electric model of powertrain: the Electric motor and the Hybrid Electric Source (HES). The given studied vehicle has a HES to processing in real-time. It is, therefore, interesting to define a flexible and fast management system, which can process the available sub-components. We use, therefore, a multi-agent system (MAS) to intelligently manage the available energy in a Fuel Cell (FC) and Super-capacitor (SC). During the simulation stage, we seek to apply an online prediction and see how the system reacts in terms of optimized power. Comparing also the results found by the adopted method to others of the same case studied. This allowing well define the most efficient and suitable laws of management for an HEV.