Genetic-based fuel-saving-delay-reduction models for autonomous heavy-duty vehicles platoons

• A validated database with 140 configurations and 560 drag coefficients improves AHDV platoon aerodynamics analysis. • Novel merging strategies and a FUEL-DEL model optimize fuel efficiency and reduce delays in AHDV platooning. • A hierarchical iterative method enhances real-time merging strategy evaluation for large-scale AHDV platoons. Truck platooning, a technology keeping autonomous trucks on dedicated lanes at synchronized inter-vehicle spacings and speeds, reduces aerodynamic drag, fuel consumption, and carbon emissions, as well as enhances transportation capacity. Recently much research attention has been focused on efficient strategies to control and merge autonomous heavy-duty vehicles (AHDVs) into platoons. This study integrates aerodynamic characteristics and innovative AHDV merging strategies into AHDV platoon operations. A synergistic approach, combining experimental tests and Computational Fluid Dynamics (CFD) simulations, is employed to determine aerodynamic drag coefficients in AHDV platoons under various conditions. Genetic-based fuel-saving-delay-reduction (FUEL-DEL) models are developed to maximize fuel efficiency and minimize travel delay for merging-platooning strategies. A case study of the FUEL-DEL models reveals that inter-vehicle spacings and distance of a leading AHDV to its exit of a shared dedicated truck lane have significant impacts on fuel efficiencies and travel delays. The FUEL-DEL models, integrated with hierarchical iterative optimization and layered-round algorithms, are extended to optimize multi-AHDV platoons. These models effectively reduce computational complexity in AHDV platooning and provide a practical tool for evaluating and optimizing merging-platooning strategies.