Enhancing performance of Parallel Hybrid Electric Vehicles using Powell's Artificial Bee Colony method.

Abstract

Hybrid Electric Vehicles (HEVs) demonstrate superior fuel efficiency and reduced emissions in comparison to conventional vehicles. To further enhance the HEV performance, Powell's based Artificial Bee Colony (ABC) heuristic approach is used. Powell's ABC focuses on the improved local search ability and increased speed of convergence. The multi parameter optimization approach with the PNGV constraints for the four differently weighted objective function parameters, the experiments were carried out for most generally used driving cycles FTP, ECE-EUDC and UDDS. Compared with the initial values, the proposed approach gives the improvement in the fuel efficiency by 10.03 % and the emissions are reduced to a maximum of 18.4 % and improved overall vehicle efficiency is 11.1 % for the ECE-EUDC driving cycle. For the UDDS driving cycle, fuel efficiency can be improved by 18.2 % and the emissions are reduced to a maximum of 43.24 %, improved overall vehicle efficiency 10.1 %. For FTP driving cycle fuel economy by 39.98 % and the emissions are reduced to a maximum of 43.75 %, improved overall vehicle energy efficiency up to 11.6 %. The findings indicate that Powell's ABC approach achieves faster convergence to a notably more precise final solution across various typical driving cycles compared to conventional methods.