Current Volume 9
A Comprehensive Survey on Fuel Consumption Optimization: Machine Learning, Energy Management, and Emerging Powertrain Technologies
Abstract
This paper presents a comprehensive survey of recent advances in fuel consumption optimization across multiple transportation domains, synthesizing findings from 18 peer-reviewed studies published between 2007 and 2025. The reviewed literature spans data-driven prediction models for marine vessels, equivalent consumption minimization strategies (ECMS) for fuel cell hybrid vehicles (FCHVs), fuzzy logic and particle swarm optimization for range extender vehicles, alternator control strategies for conventional vehicles, energy consumption standard analysis, hydrogen consumption studies under varying state-of-charge (SOC) conditions, sail-assisted ship routing, real-time fuel monitoring systems, comparative powertrain analysis across battery electric (BEV), fuel cell (FCEV), and internal combustion engine (ICEV) vehicles, machine learning models for fuel prediction, eco-driving cooperative strategies, and hardware-in-the-loop (HIL) validation of hybrid architectures. Key findings demonstrate that hybrid machine learning and physics-informed approaches consistently outperform purely rule-based strategies, achieving fuel savings of 7–20% across diverse vehicle and vessel classes. Emerging trends include connected vehicle intelligence (V2X), reinforcement learning-based energy management, and federated learning for privacy-preserving fleet optimization. This survey identifies critical research gaps and outlines a unified framework for next-generation fuel consumption optimization systems.
Keywords
Fuel Consumption Optimization, Machine Learning, Energy Management, Hybrid Electric Vehicles, Fuel Cell Vehicles, Eco-Driving, Neural Networks, ECMS, Particle Swarm Optimization, V2X, Maritime Energy Efficiency
Citations
IRE Journals:
Shankar G "A Comprehensive Survey on Fuel Consumption Optimization: Machine Learning, Energy Management, and Emerging Powertrain Technologies" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2963-2972 https://doi.org/10.64388/IREV9I11-1718015
IEEE:
Shankar G
"A Comprehensive Survey on Fuel Consumption Optimization: Machine Learning, Energy Management, and Emerging Powertrain Technologies" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718015
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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