Data-driven Modeling and Control Framework for Multi-Fuel Compression-Ignition Engines

<div class="section abstract"><div class="htmlview paragraph">Controlling the combustion phasing of a multi-fuel compression ignition engine in varying ambient conditions, such as low temperature and pressure, is a challenging problem. Traditionally, engine control is achieved by performing experiments on the engine and building calibration maps. As the number of operating conditions increase, this becomes an arduous task, and model-based controllers have been used to overcome this challenge. While high-fidelity models accurately describe the combustion characteristics of an engine, their complexity limits their direct use for controller development. In recent years, data-driven models have gained much attention due to the available computation power and ease of model development. The accuracy of the developed models, which, in turn, dictates the controller’s performance, depends on the dataset used for building them. Several actuators are required to achieve reliable combustion across different operating conditions, and obtaining extensive experimental datasets across all these conditions can be difficult. This work proposes utilizing a dataset from a high-fidelity model such as CFD to build an approximate Gaussian Process Regression (GPR) model with the Variational free-energy (VFE) methods. The developed model is then used for guided engine testing and controller development. Simulations using experimental and CFD datasets to demonstrate combustion phasing tracking performance were performed. Using the proposed methodology, a root mean square error of less than 1 degCA was obtained across the developed maps.</div></div>