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Enhancing Performance of Hybrid Electric Vehicle Using Optimized Energy Management Methodology

EasyChair Preprint no. 8608

17 pagesDate: August 6, 2022


The hybrid electric vehicle's power management strategy (PMS) and fuel efficiency are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference approach and a hybrid power management strategy are developed (ANFIS). A significant advancement in controlling electricity across multiple energy sources is artificial intelligence (AI). A proton exchange membrane fuel cell (PEMFC) serves as the major energy source in the hybrid power supply, with a battery bank and an ultracapacitor serving as electric storage systems. The stress on each energy source is calculated using the Haar wavelet transform technique. Simulink and MATLAB are used to create the suggested model. The results of the simulation show that the suggested plan is able to meet the power requirements of a typical driving cycle. Evaluations of the various PMS have been done based on their power consumption, overall efficacy, ultracapacitor and battery state of charge, stress placed on hybrid power sources, and stability of the DC bus.

Keyphrases: a hybrid electric vehicle, ANFIS, ECMS, Haar wavelet transform, Hydrogen Consumption, Power Management Scheme, system efficiency

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Mahendra Bagde and Anjali Jawadekar},
  title = {Enhancing Performance of Hybrid Electric Vehicle Using Optimized Energy Management Methodology},
  howpublished = {EasyChair Preprint no. 8608},

  year = {EasyChair, 2022}}
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