Simulated and Experimental Verification of Fuel-Efficient Truck Platooning With Model Predictive Control Under Grade and Traffic Disturbances

Truck platooning closely regulates gaps between heavy-duty freight trucks to exploit slipstream effects for reducing aerodynamic friction—and therefore reducing engine effort and fuel usage. Currently deployed applications of this have been classically actuated through error-correcting PID feedback loops with connectivity amongst trucks in a fleet to form a connected and adaptive cruise control law that attenuates disturbances between trucks to maintain tolerable gaps. Typically, performance of such systems is challenged by difficult, albeit not uncommon, transients when under traffic conditions and when under road grade variations. Because of this, such platooning control requires attentive and trained drivers to disengage the adaptive cruise control—which limits its potentials for reducing driver load. More advanced longitudinal motion planning under predictive optimal control can push for higher levels of autonomy under a larger range of scenarios, as well as improve fuel efficiency. Here, model predictive control for fuel-performant truck platooning is vetted in both simulation and experimentation for representative traffic and road grade routes. Several approaches are used exploiting physics-based models with and without the powertrain system, and neural network-encoded models. The fuel benefits of aerodynamic platooning are isolated from the more general eco-driving approach, which already provides fuel benefit to trucks by smartly selecting truck velocity. Results from simulation and validation in experimentation are presented—showing up to 6% benefit in fuel economy through eco-driving and an additional 3% achievable through platooning. Observed losses in fuel performance are explained by energy dissipation from braking.