The use of Wankel rotary engines as a range extender has been recognised as an appealing method to enhance the performance of Hybrid Electric Vehicles (HEV). They are effective alternatives to conventional reciprocating piston engines due to their considerable merits such as lightness, compactness, and higher power-to-weight ratio. However, further improvements on Wankel engines in terms of fuel economy and emissions are still needed. The objective of this work is to investigate the engine modelling methodology that is particularly suitable for the theoretical studies on Wankel engine dynamics and new control development. In this paper, control-oriented models are developed for a 225CS Wankel rotary engine produced by Advanced Innovative Engineering (AIE) UK Ltd. Through a synthesis approach that involves State Space (SS) principles and the artificial Neural Networks (NN), the Wankel engine models are derived by leveraging both first-principle knowledge and engine test data. We first re-investigate the classical physics-based Mean Value Engine Model (MVEM). It consists of differential equations mixed with empirical static maps, which are inherently nonlinear and coupled. Therefore, we derive a SS formulation which introduces a compact control-oriented structure with low computational demand. It avoids the cumbersome structure of the MVEM and can further facilitate the advanced modern control design. On the other hand, via black-box system identification techniques, we compare the different NN architectures that are suitable for engine modelling using time-series test data: 1) the Multi-Layer Perceptron (MLP) feedforward network; 2) the Elman recurrent network; 3) the Nonlinear AutoRegressive with eXogenous inputs (NARX) recurrent network. The NN models overall tend to achieve higher accuracy than the MVEM and the SS model and do not require a priori knowledge of the underlying physics of the engine.
Bibliographical noteGenerated from Scopus record by KAUST IRTS on 2023-09-21
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Automotive Engineering
- Industrial and Manufacturing Engineering