Hybrid Power Management Strategy with Fuel Cell, Battery, and Supercapacitor for Fuel Economy in Hybrid Electric Vehicle Application

Abstract

The power management strategy (PMS) is intimately linked to the fuel economy in the hybrid electric vehicle (HEV). In this paper, a hybrid power management scheme is proposed; it consists of an adaptive neuro-fuzzy inference method (ANFIS) and the equivalent consumption minimization technique (ECMS). Artificial intelligence (AI) is a key development for managing power among various energy sources. The hybrid power supply is an eco-acceptable system that includes a proton exchange membrane fuel cell (PEMFC) as a primary source and a battery bank and ultracapacitor as electric storage systems. The Haar wavelet transform method is used to calculate the stress (σ) on each energy source. The proposed model is developed in MATLAB/Simulink software. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, i.e., Highway Fuel Economy Test Cycle (HWFET) and Worldwide Harmonized Light Vehicles Test Procedures (WLTP—Class 3), for testing the vehicle performance, and assessment has been carried out for various PMS based on the consumption of hydrogen, overall efficiency, state of charge of ultracapacitors and batteries, stress on hybrid sources and stability of the DC bus. By combining ANFIS and ECMS, the consumption of hydrogen is minimized by 8.7% compared to the proportional integral (PI), state machine control (SMC), frequency decoupling fuzzy logic control (FDFLC), equivalent consumption minimization strategy (ECMS) and external energy minimization strategy (EEMS).