Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine

• Main Authors: Song Hu, Stefano d’Ambrosio, Roberto Finesso, Andrea Manelli, Mario Rocco Marzano, Antonio Mittica, Loris Ventura, Hechun Wang, Yinyan Wang
• Format: Article
• Language: English
• Published: MDPI AG 2019-09-01
• Series: Energies
• Subjects: model-based; control; diesel engine; ANN; physics-based model; semi-empirical model
• Online Access: https://www.mdpi.com/1996-1073/12/18/3423

A comparison of four different control-oriented models has been carried out in this paper for the simulation of the main combustion metrics in diesel engines, i.e., combustion phasing, peak firing pressure, and brake mean effective pressure. The aim of the investigation has been to understand the potential of each approach in view of their implementation in the engine control unit (ECU) for onboard combustion control applications. The four developed control-oriented models, namely the baseline physics-based model, the artificial neural network (ANN) physics-based model, the semi-empirical model, and direct ANN model, have been assessed and compared under steady-state conditions and over the Worldwide Harmonized Heavy-duty Transient Cycle (WHTC) for a Euro VI FPT F1C 3.0 L diesel engine. Moreover, a new procedure has been introduced for the selection of the input parameters. The direct ANN model has shown the best accuracy in the estimation of the combustion metrics under both steady-state/transient operating conditions, since the root mean square errors are of the order of 0.25/1.1 deg, 0.85/9.6 bar, and 0.071/0.7 bar for combustion phasing, peak firing pressure, and brake mean effective pressure, respectively. Moreover, it requires the least computational time, that is, less than 50 μs when the model is run on a rapid prototyping device. Therefore, it can be considered the best candidate for model-based combustion control applications.