Deep Reinforcement Learning to Discover Multi-Fuel Injection Strategies for Compression Ignition Engines

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1 Scopus Citations

Abstract

Over the past several decades, regulation of compression ignition engine emissions has become increasingly stringent as concern about the environmental and health implications of these emissions has grown. These changing constraints have led to a series of new, alternative fuel injection strategies that aim to maintain power output while reducing in-cylinder generated emissions by operating in the low-temperature combustion (LTC) regime. These advanced injection strategies are created and retuned for individual combinations of engine geometry, fuel, and emissions constraints. Deep reinforcement learning has been shown to be an effective alternative to traditional optimization approaches for highly combinatorial control problems, such as discovering the optimal injection schedules for compression ignition engines. In this study, we deploy a previously presented deep reinforcement learning framework to iteratively optimize a series of engine geometries over a range of increasingly strict NO x emissions regulations. We then examine the resulting injection schedules. We discuss the potential for using this deep reinforcement learning framework for fuel selection screening and for discovering unique injection strategies for different engine geometries and future emissions standards.

Original languageAmerican English
Pages (from-to)3985-4007
Number of pages23
JournalInternational Journal of Engine Research
Volume24
Issue number9
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© IMechE 2023.

NREL Publication Number

  • NREL/JA-2C00-83440

Keywords

  • compression ignition engine
  • Deep reinforcement learning
  • emissions reduction
  • injection strategy
  • multi-fuel
  • multi-pulse

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