Energy Optimization in Hybrid Electric and Plug-in Hybrid Vehicles using Machine Learning and Intellegent Transportation Systems

"Revolutionizing" energy optimization in Hybrid Electric Vehicles (HEVs) and Plug-In Hybrid Electric Vehicles (PHEVs) is critical to achieving sustainable transportation solutions. While these vehicles are more energy-efficient than traditional ones, optimizing their energy consumption under dynamic driving conditions remains a complex challenge. This paper introduces an innovative framework that integrates Intelligent Transportation Systems (ITS) with Machine Learning (ML) to enhance energy management in HEVs and PHEVs. By utilizing real-time traffic, environmental, and vehicle data, the ITS-ML framework intelligently adjusts route planning, energy distribution, and powertrain control, ensuring maximum efficiency and minimal emissions. Through comprehensive simulations, significant improvements in fuel efficiency and reductions in emissions were observed, particularly in urban and suburban settings. Additionally, the ITS-ML integration offers deeper insights into user behavior, enabling continuous refinement of energy management strategies. This approach represents a transformative leap towards smarter, more sustainable transportation systems. Future research will focus on real-world applications, the integration of renewable energy, and further optimization of machine learning models to push the boundaries of energy efficiency and performance.