Abstract
The present study provides a comprehensive overview of energy management strategies (EMS) in urban transportation, with a focus on their application in emerging electric vehicle (EV) technologies, particularly hybrid electric vehicles (HEVs). It presents a comparative analysis of global optimization methods—such as particle swarm optimization (PSO) and genetic algorithms (GA)—and rule-based approaches, including fuzzy logic and Boolean logic. The objective is to evaluate their effectiveness when the electric motor (EM) and internal combustion engine (ICE) operate in hybrid mode, using key performance indicators (KPIs) derived from MATLAB simulations. By determining the most suitable operating mode— whether to use the EM, the ICE, or both—the system aims to optimize energy use, enhance driver experience, reduce ICE dependency and fuel consumption, and support environmental sustainability. The study also explores the potential of emerging technologies like artificial intelligence (AI) and machine learning (ML) to further improve EMS decision-making.