Energy efficiency has become a major issue in modern trade, business and environmental perception. While the next generation of zero emission propulsion systems are still under development, it is already possible to increase fuel efficiency in regular vehicles by applying a more fuel efficient driving behaviour. This particularly holds true for transport companies, where even small percentage savings can accumulate to huge absolute savings. Although there are common fuel efficiency guidelines, they are often imprecise and not adapted to a specific vehicle. Furthermore drivers may not even know the fuel efficiency rules or lack the motivation to apply them in practice. In this paper, an online driving assistance system is presented that assist drivers during their journeys by giving them fuel efficiency guidelines that are suited for the current situation and vehicle. The driver assistance system uses an internal manufacturer independent model that can adapt to the current vehicle solely based on online CAN-Bus data.

No abstract

Major obstacles for electric vehicles are the relatively short range and insufficient infrastructure to sustain long travels among other challenges. While batteries and other technologies, that enable future vehicles to overcome the difficulties, are in development, it is already possible to decrease energy consumption by applying a more energy efficient driving behavior. Furthermore, the rise of advanced perception and V2X technologies have opened up new possibilities for safety, but also energy efficiency applications. This publication proposes a model predictive optimization approach that makes use of a power-Train model, sequences of traffic lights and other vehicles to compute energy efficient velocity and gear shift profiles over a finite optimization horizon. A stagewise forward-backward Dynamic Programming approach is used for optimization. In order to decrease the search space, the optimization works with alternating state components among other techniques. We will also introduce the REM 2030 electric vehicle that our project partners have developed in the project REM 203

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

Energy efficiency has become a major issue in trade, transportation and environment protection. While the next generation of zero emission propulsion systems are still under development, it is already possible to increase fuel efficiency in regular vehicles by applying a more fuel efficient driving behaviour. This paper proposes a model predictive A* optimization that makes use of a power-train model and the topography for the road ahead. The main scientific contribution is the development of admissible and monotonic non-trivial heuristics that allow A* to be used in an efficient manner while preserving global optimality. Simulations show that the heuristics guided optimization traverses a significantly smaller search space than dynamic programming without heuristics while preserving global optimality