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A hybrid power management strategy is proposed for hybrid electric vehicles, combining adaptive neuro-fuzzy inference method and equivalent consumption minimization technique to optimize fuel economy.

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System
MFC

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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).

Key findings

  • The proposed scheme meets the power demand of a typical driving cycle, including Highway Fuel Economy Test Cycle and Worldwide Harmonized Light Vehicles Test Procedures.
  • The use of a proton exchange membrane fuel cell as a primary source, combined with a battery bank and ultracapacitor, results in improved fuel economy.
  • The Haar wavelet transform method is used to calculate the stress on each energy source, optimizing their performance.

Keywords

Energy managementAutomotive engineeringDriving cycleHybrid powerPowertrainFuel efficiency

Identifiers

DOI
10.3390/en15124185
Journal
Energies
Year
2022