AI-Guided Energy Management in Electric and Hybrid Vehicles for Achieving Sustainable and Carbon-Neutral Transportation Ecosystems

Abstract

This paper investigates the role of artificial intelligence (AI) in optimizing energy management systems for electric and hybrid electric vehicles (EVs and HEVs), focusing on three key areas: charging cycles, route planning, and regenerative braking. As transportation systems globally shift towards sustainability, improving the energy efficiency of EVs and HEVs is crucial to reducing carbon emissions and promoting carbon-neutral ecosystems. The study employs AI-driven algorithms to optimize the charging process based on real-time data, predicts and adjusts routes to minimize energy consumption, and enhances regenerative braking efficiency. The experimental design integrates machine learning, reinforcement learning, and deep learning models to analyze and optimize these components. The findings reveal a 15% reduction in energy consumption, a 12% reduction in carbon emissions, and an 18% improvement in regenerative braking efficiency. Additionally, AI-based route planning resulted in a 10% improvement in energy efficiency compared to traditional navigation systems. The study underscores the importance of integrating these AI technologies to maximize vehicle performance while contributing to the achievement of carbon-neutral transportation. The results highlight the potential of AI in creating sustainable, efficient, and environmentally friendly transport systems, suggesting future research avenues in system-wide integration and real-world deployment.