<![CDATA[The growing demand for sustainable transportation solutions has led to significant advancements in hybrid electric vehicles (HEVs). However, optimizing energy management in these systems remains a critical challenge. This study explores the application of fuzzy logic-based energy management strategies to optimize the performance of hybrid electric vehicles. The primary aim is to develop a real-time adaptive system capable of improving energy efficiency and reducing CO2 emissions by optimizing power distribution between the internal combustion engine and the electric motor. The research employs a quantitative approach, using both simulations and real-world testing of selected HEV models. Data on energy consumption and CO2 emissions were collected and analyzed across various driving cycles. The results indicate that the fuzzy logic-based energy management system significantly reduced energy consumption by up to 21.6% and CO2 emissions by 22.2% compared to traditional energy management systems. The fuzzy logic system demonstrated superior adaptability to dynamic driving conditions, leading to enhanced vehicle performance and sustainability. This study concludes that fuzzy logic offers a robust solution for optimizing energy management in hybrid vehicles, contributing to reduced fuel consumption and environmental impact. Future research should focus on integrating machine learning techniques and expanding the system’s application to a wider range of hybrid and electric vehicle models.]]>
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