Reuse historic costs in dynamic programming to reduce computational complexity in the context of model predictive optimization

Energy efficiency has become a major issue in trade, transportation and environment protection. While the next generation of zero emission propulsion systems still have difficulties in reaching similar travel distances as combustion engine propulsion systems, it is already possible to increase fuel efficiency in regular vehicles by applying a more fuel efficient driving behaviour. An adapted Dynamic Programming approach is used to calculate optimal behaviour profiles for the road ahead within a finite optimization horizon. The main purpose of this publication is the development of a strategy to reuse historic minimal costs in order to reduce the computational complexity of future optimization steps. The percent reduction is deterministic and increases with the discretization degree of the optimization horizon.