摘要：The electrification of drivetrains of conventional vehicles plays a decisive role in reducing fuel consumption. At the same time decreasing pollutant emission limits must be met also under real driving conditions. This trade-off between fuel consumption and pollutant emissions needs to be optimized, which results in powertrains with increasing complexity. A holistic energy and emission management is needed to control such systems in a way that the fuel consumption is minimized while emission limits are respected.Mathematical optimization methods are difficult to apply in real-time applications due to high computational and calibration demands. Self-learning algorithms, on the other hand, seem to be a suitable solution for such optimization problems.In this paper a control strategy for a hybrid electrical vehicle is presented, consisting of a decision-making agent, trained on different test drives with Reinforcement Learning. For these, the Proximal Policy Optimization method was applied. The strategy controls the torque-split between the combustion engine and electric motor, the power of an electrically heated catalyst and internal engine measures. The method is demonstrated in a simulation framework based on a Diesel P0-HEV with a SCR exhaust gas aftertreatment system. In comparison to a reference strategy a fuel reduction of 3.1 % averaged over the test data set was achieved.
