文章基本信息

标题：Energy and Emission Management of Hybrid Electric Vehicles using Reinforcement Learning
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作者：Johannes Hofstetter ; Hans Bauer ; Wenbin Li 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：29
页码：19-24
DOI：10.1016/j.ifacol.2019.12.615
语种：English
出版社：Elsevier
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.
关键词：KeywordsHybrid VehiclesEnergy Management SystemsAutomotive ControlAutomotive EmissionsAdaptive AlgorithmsMultiobjective OptimizationReinforcement Learning