An Intelligent Coot Bird Optimization-based Energy Management Strategy in Hybrid Electric Vehicle

Abstract

Energy Management in Hybrid Electric Vehicles (HEV) is a cost-effective tool for improving the equipment’s energy efficiency and lowering energy costs to the consumer. Accordingly, it is important to quantify the fuel savings considering the vehicle constraints. The Vehicle power split has to be in such a way that every moment, the net power demand on a final drive is fulfilled by either the internal combustion engine alone or the electric motor alone or in combination. This dual momentum nature, the complex composition, and the operation modes in a series-parallel HEV create barriers in traditional methods. Therefore, meta-heuristic algorithm-based simulations are required to check the feasibility of the proposed design. Coot Bird Optimization (CBO) is one of the recent meta-heuristic methods acquired from the bird swarm named Coot. The coot leaders are represented by high-quality solutions and the coot members correspond to low-quality solutions. In order to get to a food source, the swarm advances towards a group of prominent leaders. This chosen technique CBO has proven to optimize the fuel depletion in each drive cycle. Henceforth, the proper energy diversification in the vehicle is substantiated by the proposed method