Look-Ahead Platooning through Guided Dynamic Programming

It has already been shown that look-ahead control can result in fuel savings up to 3% and platooning can save around 7 %. The aim of this thesis is to combine these two strategies. Various strategies that can be used for this purpose are analysed. Fuel and time optimal state utilises the momentum or the stored energy of the truck and at the same time keeps the distance between the trucks as small as possible. This thesis presents a cooperative form of decentralised predictive controller to achieve this. Dynamic programming strategy, which is used to solve it has a very high computational complexity due to the number of states and its possible values. To avoid this, an iterative dynamic programming algorithm which operates only on a small region of search space was developed. The search space is made flexible, which improves the runtime and performance of the algorithm. The algorithm takes less than one tenth of the time taken by the initial implementation of dynamic programming. This resulted in a controller that decided weather platooning or look-ahead control or partly both should take the leading role in controlling the trucks for the best performance. Simulations with the fuel and time optimal velocity profiles thus obtained showed fuel savings of 3.9% for a two platoon system on a real road simulation.