Application of Fuzzy Logic for Power Management in Hybrid Vehicles

Fernanda C. Corrêa, Jony E. Javorski, Ludmila C. A. Silva, Fabio Mazzarioll Santiciolli, Franco Giuseppe Dedini


The increasing number of cars may be causing serious effects to the environment and to humans, such as pollution, global warming, and depletion of oil reserves, among others. This situation encourages the research for new energy forms and devices with higher energy efficiency. The adoption of hybrid propulsion technology has contributed, considerably, to reducing gases such as oxides of carbon, nitrogen and sulfur and the reduction of particulate materials. Beyond, the hybrid electric vehicle (HEV) maintains the characteristics attributed to conventional vehicles such as performance, safety and reliability. The term "hybrid” derives from the combination of two or more power sources, and the most common combination is through of an internal combustion engine (ICE), commonly used in conventional vehicles, together with the battery and electric motor (EM) used in EVs (Electric Vehicles). In general, the main reason to use electric hybrid architecture is the additional degree of freedom due to the presence of an additional energy source, which implies that, at each instant, the power required by the vehicle can be provided by one of these sources, or a combination of both. Choose the correct combination is usually a complex task. For a HEV present satisfactory operation (performance and emission reduction) is important that the architecture and components of HEVs are optimized, and occurs an appropriate choice of power management strategy. In this work is carried out the development and analysis of power management strategies in a HEV to minimize its fuel consumption and consequently emissions. Is developed one management strategy using fuzzy systems, and its results is analyzed varying the vehicle mass. The results of this work allow to view when it is triggered each propulsion system, and to analyze the consumption of fuel for each power management strategy.

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