Engine modelling architecture study for hybrid electric vehicle diagnosis application

Abstract

Model-based control, calibration, and diagnosis are widely used in hybrid electric vehicles (HEVs) studies. For the real-time application, the contradiction lies in the model accuracy and fast response. This problem is particularly evident in engine modeling due to its complexity. In this study, three types of engine model architectures were studied for HEV Hardware In the Loop Simulation (HILS) diagnosis application. It shows that in terms of the real-time factor, the MAP model, obtaining all engine outputs only by looking up maps, has the best real-time performance. Both Mean Value Model (MVM) and Fast Running Model (FRM), with less than 3% steady-state error and preserving the engine's dynamic characteristics, have better real-time performance than the detailed model. What is more, the real-time performance of MVM is about 14% faster than that of FRM. MVM-based and FRM-based vehicle models were verified respectively in the HILS for the fault classification using support vector machine (SVM) and for the engine's misfire fault diagnosis. Since the MVM combines multiple detailed cylinders into a single mean value cylinder through neural network training, the MVM is typically used in diagnosis situations where fast computation speed is especially important and the detailed characterization of engine processes is considered relatively unimportant, such as vehicle-level fault diagnosis applications involving control system design, thermal management system fault, and power system fault, etc. Since the FRM reduces cylinder calculations by using the “cylinder translation method”, which preserves the working process within a single-cylinder cycle, it can also be used for fault diagnosis applications requiring engine performance with a crank-angle resolution, such as cylinder pressure diagnostics, combustion diagnostics, and timing control of valve and fuel injection, etc.