Energy and Emission Management of Hybrid Electric Vehicles using Reinforcement Learning

Johannes Hofstetter, Hans Bauer, Wenbin Li, Georg Wachtmeister

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)19-24
Number of pages6
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume52
Issue number29
DOIs
StatePublished - 2019
Event13th IFAC Workshop on Adaptive and Learning Control Systems, ALCOS 2019 - Winchester, United Kingdom
Duration: 4 Dec 20196 Dec 2019

Keywords

  • Adaptive Algorithms
  • Automotive Control
  • Automotive Emissions
  • Energy Management Systems
  • Hybrid Vehicles
  • Multiobjective Optimization
  • Reinforcement Learning

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