Chimp optimization-based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

Today's concern about climate change and the country's economic growth have made adopting hybrid electric vehicles (HEV) a worthy solution. Since the gas emissions of HEVs are closely related to their motor performance, developing an effective HEV motor control technique is essential. The performance and acceleration of an HEV motor are determined by the internal combustion engine (ICE) and the electronic throttle plate (ETP), which employ an air-fuel mixture for vehicle propulsion. In order to deal with this problem, this article presented an optimal fuzzy controller that controls the throttle plate's angular position and motor speed in HEVs. However, the efficiency and reliability of fuzzy controllers depend upon their gains factor. So, a different heuristic algorithm is employed to self-tuning the fuzzy logic controller (FLC) gain factor. The performance of the suggested ChOA-fuzzy-based controller was evaluated utilizing various control error indices and time-domain stability. A comprehensive controller performance analysis using different metaheuristic techniques has been carried out to validate the proposed scheme. The findings show that the suggested ChOA-fuzzy-based controller performs better than other techniques. HEV motor performance and acceleration depend on the internal combustion engine (ICE) and electronic throttle plate (ETP), which use air-fuel mixture for propulsion. To solve this issue, this study proposed an optimum fuzzy controller that regulates HEV throttle plate angular position and motor speed. Since, fuzzy controllers' reliability and effectiveness rely on their gains factor. Therefore, to self-tune the fuzzy logic controller (FLC) gain factor, a heuristic approach is used. The findings show that the suggested ChOA-fuzzy-based controller performs better.image