Deep learning based model predictive control for compression ignition engines

Emissions Control Challenges for Compression Ignition Engines

As emission standards continue to evolve, it is clear that future engine control strategies will involve the integration of combustion optimization, fuel refinement and advanced exhaust after-treatment technologies. The West Virginia University (WVU) Center for Alternative Fuels Engines and Emissions (CAFEE) continues to engage the challenge of future regulation in a multi-pronged approach, inv...

Homogenous Charge Compression Ignition (HCCI) Engines

DeepMPC: Learning Deep Latent Features for Model Predictive Control

Designing controllers for tasks with complex nonlinear dynamics is extremely challenging, time-consuming, and in many cases, infeasible. This difficulty is exacerbated in tasks such as robotic food-cutting, in which dynamics might vary both with environmental properties, such as material and tool class, and with time while acting. In this work, we present DeepMPC, an online real-time model-pred...

Model-based cold-start speed control scheme for spark ignition engines

This paper presents a model-based control scheme to the cold-start speed control in spark ignition (SI) engines. The multi-variable control algorithm is developed with the purpose of improving the transient performance of the starting engine speed: the control inputs are the fuel injection, the throttle and the spark advance (SA), while the engine speed and the air mass flow rate are the measur...

Development of a Flame Propagation Model for Dual-Fuel Partially Premixed Compression Ignition Engines

A new combustion model is developed and applied to simulate combustion in dual-fuel engines in which the premixed natural gas is ignited by the combustion flame initiated by a diesel spray. The model consists of a diesel auto-ignition model and a flame propagation model. A G-equation model previously developed to simulate SI engine combustion was incorporated with an auto-ignition model to simu...