Gerald Steinmaurer

Adaptive Inverse Torque Control of a Diesel Engine Using Adaptive Mapping Update

Torque control is a basic element of engine control systems, in particular since it has become a standard interface for different functionalities. Torque control is also a critical requirement emission test cycle simulation on test benches. This torque control is usually reached by extensive, physical based modeling of the vehicle. This paper presents an approach to avoid this effort and to obtain a dramatic reduction of the parametrization work, by first determining an approximated model and then updating it online during operation. This model is than used for a stable inverse control. To handle model uncertainties and perturbation a correction feedback, with robustifying effect, is added to the control structure. This approach is detailed using data and measurements on a BMW M47D production diesel engine on a dynamic test bench.

Overview: a simulation based metaheuristic optimization approach to optimal power dispatch related to a smart electric grid

The implementation of intelligent power grids, in form of smart grids, introduces new challenges to the optimal dispatch of power. Thus, optimization problems need to be solved that become more and more complex in terms of multiple objectives and an increasing number of control parameters. In this paper, a simulation based optimization approach is introduced that uses metaheuristic algorithms for minimizing several objective functions according to operational constraints of the electric power system. The main idea is the application of simulation for computing the fitness-values subject to the solution generated by a metaheuristic optimization algorithm. Concerning the satisfaction of constraints, the central concept is the use of a penalty function as a measure of violation of constraints, which is added to the cost function and thus minimized simultaneously. The corresponding optimization problemis specified with respect to the emerging requirements of future smart electric grids.

Extension of a linear optimal control strategy for HEV

Since the last decade, hybrid electric vehicles have been and are introduced increasingly by automotive industry as they provide substantial improvements in fuel consumption. However, the optimal power distribution between the different energy sources in a HEV to achieve high efficiency is a non trivial problem. In a former work, an approximate solution to the generic optimization task by a two-stage approach based on linear programming and switching strategies was introduced, which is extended within this work. First, the opportunity to declutch and switch off the internal combustion engine is considered, which extends the linear program to a mixed integer linear program and leads to additional improvements in fuel consumption. The second extension introduces a receding horizon strategy that considers the current and a target state of charge of the battery at the end of the horizon during the top level optimization. Thus a complete knowledge of the driving cycle is not required but only a prediction for a shorter horizon. Furthermore, errors caused by model-plant mismatch and prediction can be compensated by the SOC feedback. Both extensions result in a minimum increase in computational effort and allow to formulate the optimization as mixed integer linear program and solve it by real-time capable solvers. As application example a parallel hybrid electric vehicle with realistic models of battery and powertrain and the opportunity to declutch the internal combustion engine is considered. The method and the proposed extensions are evaluated in simulation and experimental in which satisfactory results could be achieved in both cases.