Adaptive honey badger algorithm based on maximize power tracking of PEMFC based electric vehicle application

Abstract This study introduces a new controller based on Adaptive Honey Badger Algorithm (AHBA) for Maximum Power Point Tracking (MPPT) to enhance the performance of electric vehicle (EV) system which utilizes Proton Exchange Membrane Fuel Cell (PEMFC). The PEMFC technology possesses several desirable attributes such as high energy-conversion efficiency, low operating temperature, rapid response and long service life. It is, however, highly sensitive to differences in operating conditions including cell temperature, fuel pressure, and fuel permeation which can lead to changes in power, impact tracking accuracy and system efficiency. To solve these problems, an AHBA based MPPT control strategy is developed for PEMFC system. The proposed method is an intelligent control of converter duty cycle, which can help to mitigate the oscillations and tracking errors while tracking maximum power point accurately and quickly. Proposed controller is tested using a 1.26 kW PEMFC simulation model with a DC–DC boost converter and inverter under various operating conditions. The results of the simulations clearly show the great advantages of the new approaches over conventional methods in terms of both static and dynamic performance. The AHBA-based controller offers greater tracking responsiveness, power fluctuation minimization, oscillation-free tracking, energy efficiency, power loss reduction, failure probability reduction and overall system improvement.The enhancement of the system durability, lower operating cost and better economic performance that the above improvements will bring is expected to deliver promising applications of AHBA-based MPPT in reliable and efficient PEMFC powered EV applications.