Linear Analysis of Optimal Energy Management in Hybrid Electric Vehicle using Evolutionary Algorithms

Abstract

In order to reduce greenhouse gas emissions and its detrimental effects on the environment and human health, electric vehicles, or EVs, have become a popular substitute for internal combustion engine (ICE) vehicles in recent years. However, the low battery efficiency and decreased driving range of the independent electric vehicles presented a challenge. As a result, a hybrid system that uses both electrical and internal combustion engines for propulsion was created. Electrical mode, ICE mode, or a mix of both modes can be used to run the HEV. An appropriate Energy Management System (EMS) may ensure that a hybrid electric vehicle operates more efficiently by choosing the right operating modes. To adjust the PI controller's gain parameters in the ICE, generator, and motor speed regulations, an optimisation problem is created. To solve the optimisation challenge, Naked Mole Rat (NMR) optimisation techniques are used as intelligent optimisation methods. The findings demonstrate that, in comparison, the NMR Optimisation algorithm yields superior results when it comes to the best tuning of the PI Controller in terms of speed regulations in the suggested hybrid electric vehicle model.