Modelling Fuel Consumption andNOₓEmission of a Medium Duty Truck Diesel Engine With Comparative Time-Series Methods

Mehmet Ilter Ozmen,Osman Azmi Ozsoysal,Abdurrahim Yilmaz,Cemal Baykara

doi:10.1109/access.2021.3082030

Mehmet Ilter Ozmen, Osman Azmi Ozsoysal + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3082030

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: Istanbul Technical University

Abstract

This study focuses on different intelligent time series modelling techniques namely nonlinear autoregressive network with exogenous inputs (NARX), autoregressive integrated moving average with external inputs (ARIMAX), multiple linear regression (MLR), and regression error with autoregressive moving average (RegARMA), applied on a diesel engine to predict NO x emission and fuel consumption. The experiment data are collected from a six cylinder, four stroke medium duty truck diesel engine, which is integrated on a passenger bus and operated in engine integration tests. NO x emission and fuel consumption outputs are estimated with the help of input data; exhaust gas recirculation temperature and position, engine coolant temperature, engine speed, exhaust gas pressure, common rail pressure, intake manifold air temperature and pressure, accelerator pedal percentage, engine load, turbocharger variable geometry position and speed, and selective catalytic reduction outlet temperature. NARX artificial time series neural network, MLR, ARIMAX, and RegARMA time series techniques were separately applied for the estimation NO x emission and fuel consumption outputs. The performance of the models is analyzed and evaluated with Bayesian information criterion (BIC) and root mean square error (RMSE) criteria. When the high cost and time loss of experimental testing are thought, using the intelligent modelling methodology provides far more accurate prediction and fast application abilities to analyze internal combustion engine dynamics for the control and calibration manner. As a result of the comparison of different types of modelling techniques, RegARMA technique comes to the forefront with 6707.6 BIC value with 105.58 RMSE for NO x emission model and 4026.4 BIC value with 7.93 RMSE for fuel consumption model.

Highlights

Diesel engine-originated by Rudolf Diesel in 1982-is an internal combustion engine that converts chemical energy to the mechanical power by way of the compression-ignition principle which allows the highly compressed air has a sudden ignite with the injected diesel fuel [1] that has high ignition capability
Bayesian information criterion (BIC) is used for the time series models in terms of goodness of fitting (GoF) whereas root mean square error (RMSE) is used for error calculation purpose
Investigation on the results shows that the highest estimation performance is belong to regression error with autoregressive moving average (RegARMA) which is resulted nearly with the autoregressive integrated moving average with external inputs (ARIMAX) in average

Summary

Introduction

Diesel engine-originated by Rudolf Diesel in 1982-is an internal combustion engine that converts chemical energy to the mechanical power by way of the compression-ignition principle which allows the highly compressed air has a sudden ignite with the injected diesel fuel [1] that has high ignition capability. This leads to a high compression ratio, high power and subsequent relatively low fuel consumption and emissions which rise compression-ignition to prominence. Considering the increase of emission restrictions (oncoming Euro 7-2023 for heavy duty vehicles) and fuel economy expectations, investigations are ongoing process for controlling diesel engine

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Results

Conclusion