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

Compression ignition (CI) engines typically offer high thermal efficiencies at the expense of harmful gaseous emissions such as NOx. Various strategies for reducing the emissions of CI engines have been developed over the years from exhaust gas after-treatment systems to advanced fuel injection strategies. Some of these strategies involve splitting the fuel injection into multiple pulses (e.g., MCCI) and others leverage multiple fuels with specially designed injection timing to operate in the low temperature combustion (LTC) regime that produces less emissions (e.g., RCCI, DDFS, etc.). In this talk, we show how we can train a deep reinforcement learning (RL) agent to discover multi-fuel, multi-pulse injection strategies for a reduced order model of a CI engine with the objective of maximizing engine work production while maintaining NOx emissions below a certain threshold. Results are shown and discussed for different injection strategies.