Improving the performance of a parallel hybrid electric vehicle by heuristic control method [articol]

Abstract

Hybrid Electric Vehicles (HEV) are expected as one of key solutions for mobility in the future, with reduced pollutions and better fuel economy alternative. In this paper, an analysis on Parallel HEV to reduce fuel usage and improve emission control performance, in addition to optimising the size of its key components has been presented. Various number of optimisation strategies have been proposed in literature. With respect of real time implementation, most of the papers in the literature have proposed on the use of heuristics. Despite the research advances made, the key challenge with heuristic strategies remain in achieving reasonable fuel savings without over depleting the battery state of charge at the end of the trip. To handle this Challenge, this paper offers an effective heuristic control strategy based on Artificial Bee Colony(ABC) algorithm and also in addition a modified approach, in analysing and dynamically optimizing key vehicle key component size, which influence the vehicle performance and to find a right combination of these significant parameters, which would maximize vehicle performance through reduced fuel consumption and emission. The potential of the proposed heuristic control strategy was explored over various drive cycles, which reflect different driving scenarios. Results from this analysis show, that as much as 22% fuel savings could be achieved over the UDDS driving cycle, which is the maximum, when compared with other driving cycles considered. Also in comparison to a basic ABC algorithm, the Modified Artificial Bee Colony(MABC) algorithm was found to be outperforming, in that it achieved impressive real time fuel savings and reduced emissions, without much penalty to the final battery state of charge along with reduced key vehicle components size for different driving cycles.