Energy management in autonomous vehicles: A systematic literature review with focus on conventional ICE-based and hybrid powertrains

Autonomous vehicles (AVs) are rapidly advancing, promising enhanced safety, improved traffic flow, and greater convenience. However, their high-power demands - stemming from sensors, computing units, and control systems - pose significant energy management challenges. While this review centres on conventional internal combustion engine (ICE) and hybrid powertrain architectures, relevant studies on electric and plug-in hybrid autonomous vehicles are also discussed where they offer meaningful methodological insights. Efficient energy utilization is crucial for reducing emissions and optimizing operational costs, given the transportation sector's substantial contribution to air pollution. This review examines existing research from 2018 to 2026, addressing four key questions: (1) current methods for energy management and their strengths and limitations; (2) approaches for joint traffic flow and speed prediction to inform energy strategies; (3) techniques to balance fuel efficiency, torque stability, and emissions during highway driving; and (4) strategies for managing energy at the fleet level, including vehicle platooning. Findings indicate a growing integration of intelligent control and AI techniques to improve energy efficiency. Nonetheless, challenges remain, such as system integration, real-world testing, and ensuring interoperability between autonomous and conventional vehicles. Overcoming these barriers is essential to realize safe, environmentally sustainable, and energy-efficient autonomous transportation. Future research must focus on holistic, validated solutions that address technical, societal, and environmental considerations.