Search Results

The eHorizon unit enables the possibility to get information of the road ahead in a defined prediction horizon. This data, like road gradient, curve radius, velocity limitation and road class, can be used by the onboard transmission control unit (TCU) via Controller Area Network (CAN) bus. This obtained predictive road information combined with the actual driving conditions can be used to optimize the shifting strategy by a model predictive control (MPC) algorithm which intends to reduce the fuel consumption. In order to solve the optimum problem inside the MPC with less memory, a pre-optimization based dynamic programming (PODP) approach is proposed. In this paper, the predictive gear selection (PGS) strategy will be compared to a conventional automatic gear shifting strategy in a simulation environment and validated on road by implementing it on a heavy-duty truck with a 16-speed automated manual transmission (AMT).

During transients, engines tend to produce substantially higher peak emissions like soot - the main fraction of particular matter (PM) - which are the longer the more important as the steady state emissions are better controlled. While Diesel particulate filters are normally able to block them, preventing their occurrence would of course be more important. In order to achieve this goal, however, they must be measurable. While for most emissions commercial sensors of sufficient speed and performance are available, the same is not true for PMs, especially for production engines. Against this background, in the last years the possible use of a full stream 50Hz sensor based on Laser Induced Incandescence (LII) was investigated, and the results were very encouraging, showing that the sensor could recognize transient changes undetected by conventional measurement systems (like the AVL Opacimeter) but confirmed by the analysis of combustion.

At the moment, no equipment is available for fast measurements of particulate matter (PM) from production CI engines, especially during transients. Against this background, virtual sensors may be an option, provided their precision can be validated. This paper presents a new approach to estimate PM emission based only on in-cylinder pressure data. To this end, an in-cylinder pressure trace is measured with a high resolution (0.5 CAD) and every trace is divided into 8 segments according to critical cylinder events (e.g. opening of the valves or the beginning of injection). A piecewise principle component analysis (PCA) is used to compress the information. This information is then used for PM estimation via a second order polynomial model structure. The key element is the separate use of pressure trace information before and during the early stages of combustion. The model is parameterized by steady points and transient experiments which include parts of the FTP and the NEDC.