Abstract:
Recent advancements in vehicle connectivity and advanced driver-assistance systems allow for more efficient driving in automated driving applications on the road. The pra...Show MoreMetadata
Abstract:
Recent advancements in vehicle connectivity and advanced driver-assistance systems allow for more efficient driving in automated driving applications on the road. The practice of truck platooning utilizes following distances as small as a few meters of each vehicle in a string to benefit from slipstream effects and reduce aerodynamic drag. By this, fuel economy is then improved in the vehicles. In this work, the impact of a connected and anticipative cruise controller in a truck platooning application is explored. We utilize two separate optimal controllers: 1) a time-invariant kinematic model with a first-order lag on the acceleration of the vehicle - intended strictly as a connected gap-tracking controller, and 2) a time-varying dynamic model which considers linearized aerodynamic drag terms - intended as an engine demand optimizer. We consider penalty terms in the cost functional to promote string compactness and reduce accelerations incurred, and propose a set of linear constraints which restrict truck capabilities to those of a realistic engine. We compare our optimal controllers to an Intelligent Driver Model baseline modeled after human response, and we find that our time-invariant connected truck platoon performs with 14% better fuel economy, whereas our time-varying connected truck platoon performs with 20% better fuel economy. By comparing our time-invariant and time-varying controllers, we conclude that demanded work on the engine due to maintaining a strict gap between trucks is detrimental to fuel economy.
Published in: 2020 American Control Conference (ACC)
Date of Conference: 01-03 July 2020
Date Added to IEEE Xplore: 27 July 2020
ISBN Information: