Nonlinear System Identification of a Homogeneous Charge Compression Ignition Engine

Abstract

The primary aim of this study was to develop a comprehensive multi-variable model for Homogeneous Charge Compression Ignition (HCCI) engines and evaluate its efficacy by comparing it to simulated HCCI engine data. The complexity of the engine’s nonlinear dynamics necessitated the development of appropriate models to address the associated control challenges. Multiple Inputs Single Output (MISO) models were employed to capture the pressure and temperature variables. The estimated outputs were generated using Nonlinear Autoregressive with Exogenous Inputs (NARX) and Hammerstein-Wiener (HW) models, both were used as black box models. The performance of each model was assessed using metrics such as Normalized Root Mean Square Error (NRMSE), Mean Square Error (MSE), and Akaike’s Final Prediction Error (FPE). The results highlight the effectiveness of the Hammerstein-Wiener models in accurately representing the intricate dynamics of complex combustion, as observed in HCCI engines. The consistency of these models in delivering reliable outcomes further underscores their suitability for modeling such intricate systems.