Affiliation:
1. Indian Institute of Technology Madras, India
2. Indian Institute of Technology Madras, Department of Mechanical
Engineering, India
Abstract
<div>Reinforcement learning (RL) is a computational approach to understanding and
automating goal-directed learning and decision-making. The difference from other
computational approaches is the emphasis on learning by an agent from direct
interaction with its environment to achieve long-term goals [<span>1</span>]. In this work, the RL algorithm was
implemented using Python. This then enables the RL algorithm to make decisions
to optimize the output from the system and provide real-time adaptation to
changes and their retention for future usage. A diesel engine is a complex
system where a RL algorithm can address the NO<sub>x</sub>–soot emissions
trade-off by controlling fuel injection quantity and timing. This study used RL
to optimize the fuel injection timing to get a better NO–soot trade-off for a
common rail diesel engine. The diesel engine utilizes a pilot–main and a
pilot–main–post-fuel injection strategy. Change of fuel injection quantity was
not attempted in this study as the main objective was to demonstrate the use of
RL algorithms while maintaining a constant indicated mean effective pressure. A
change in fuel quantity has a larger influence on the indicated mean effective
pressure than a change in fuel injection timing. The focus of this work was to
present a novel methodology of using the 3D combustion data from analysis
software in the form of a functional mock-up unit (FMU) and showcasing the
implementation of a RL algorithm in Python language to interact with the FMU to
reduce the NO and soot emissions by suggesting changes to the main injection
timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms
identified the operating injection strategy, i.e., main injection timing for a
pilot–main and pilot–main–post-injection strategy, reducing NO emissions from
38% to 56% and soot emissions from 10% to 90% for a range of fuel injection
strategies.</div>